Platforms to implement signal detection metrics in adaptive response-deadline procedures

ABSTRACT

Example systems, methods, and apparatus, including cognitive platforms, are provided for applying signal detection metrics in computer-implemented adaptive response-deadline procedures to data collected based at least in part on user interaction(s) with computerized tasks and/or interferences. The apparatus can include a response classifier for generating a quantifier of the cognitive abilities of an individual. The apparatus also can be configured to adapt the tasks and/or interferences to enhance the individual&#39;s cognitive abilities.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is the national stage application of International(PCT) Patent Application Serial No. PCT/US2017/042938, filed Jul. 19,2017, which claims priority benefit of U.S. provisional application No.62/364,297, entitled “SIGNAL DETECTION METRICS IN ADAPTIVERESPONSE-DEADLINE PROCEDURES,” filed on Jul. 19, 2016, and of U.S.design application numbed 29/579,480 entitled “GRAPHICAL USER INTERFACEFOR A DISPLAY SCREEN OR PORTION THEREOF,” filed on Sep. 30, 2016; eachof these three applications is incorporated herein by reference in itsentirety, including drawings.

BACKGROUND OF THE DISCLOSURE

In the normal course of aging, individuals can experience a certainamount of cognitive decline. This can cause an individual to experienceincreased difficulty in challenging situations, such as time-limited,attention-demanding conditions. In both older and younger individuals,certain cognitive conditions, diseases, or executive function disorderscan result in compromised performance at tasks that require attention,memory, motor function, reaction, executive function, decision-makingskills, problem-solving skills, language processing, or comprehension.

SUMMARY OF THE DISCLOSURE

In view of the foregoing, apparatus, systems and methods are providedfor quantifying aspects of cognition (including cognitive abilities). Incertain configurations, the apparatus, systems and methods can beimplemented for enhancing certain cognitive abilities.

Example apparatus, systems and methods are configured for applyingsignal detection metrics in computer-implemented adaptiveresponse-deadline procedures to data collected based at least in part onuser interaction(s) with computerized tasks and/or interferences. Forexample, the apparatus can include a response classifier for generatinga quantifier of the cognitive abilities of an individual. As anotherexample, the apparatus also can be configured to adapt the tasks and/orinterferences to enhance the individual's cognitive abilities.

In a general aspect, an apparatus for generating a quantifier ofcognitive skills in an individual using a response classifier isprovided. The apparatus includes a user interface; a memory to storeprocessor-executable instructions; and a processing unit communicativelycoupled to the user interface and the memory, in which upon execution ofthe processor-executable instructions by the processing unit, theprocessing unit is configured to render a task with an interference atthe user interface, one or more of the task and the interference beingtime-varying and having a response deadline, such that the userinterface imposes a limited time period for receiving at least one typeof response from an individual; and the user interface being configuredto measure data indicative of two or more differing types of responsesto the task or to the interference. The processing unit is furtherconfigured to receive data indicative of a first response of anindividual to the task and a second response of the individual to theinterference; analyze the data indicative of the first response and thesecond response to compute at least one response profile representativeof a performance of the individual; determine a decision boundary metricfrom the response profile, the decision boundary metric comprising aquantitative measure of a tendency of the individual to provide at leastone type of response of the two or more differing types of responses tothe task or the interference; and execute a response classifier based atleast in part on the computed values of decision boundary metric, togenerate a classifier output indicative of the cognitive responsecapabilities of the individual.

In another general aspect, a computer-implemented method for generatinga quantifier of cognitive skills in an individual using a responseclassifier is provided. The method includes rendering a task with aninterference at a user interface; measuring data indicative of two ormore differing types of responses to the task or to the interference;receiving data indicative of a first response of an individual to thetask and a second response of the individual to the interference. Themethod includes analyzing the data indicative of the first response andthe second response to compute at least one response profilerepresentative of the performance of the individual. The method includesdetermining a decision boundary metric from the response profile, thedecision boundary metric comprising a quantitative measure of a tendencyof the individual to provide at least one type of response of the two ormore differing types of responses to the interference. The methodincludes executing a response classifier based at least in part on thedecision boundary metric, to generate a classifier output indicative ofthe individual's cognitive response capabilities.

In another general aspect, an apparatus for enhancing cognitive skillsin an individual is provided. The apparatus includes a user interface; amemory to store processor-executable instructions; and a processing unitcommunicatively coupled to the user interface and the memory, in whichupon execution of the processor-executable instructions by theprocessing unit, the processing unit is configured to render a primarytask with an interference at the user interface, one or more of the taskand the interference being time-varying and having a response deadline,such that the user interface imposes a limited time period for receivingat least one type of response from an individual; and the user interfacebeing configured to measure data indicative of two or more differingtypes of responses to the task or to the interference. The processingunit is configured to receive data indicative of a first response of anindividual to the task and a second response of the individual to theinterference; and analyze the data indicative of the first response andthe second response to compute at least one response profilerepresentative of a performance of the individual. The processing unitis configured to determine a first decision boundary metric based atleast in part on the at least one response profile, the first decisionboundary metric comprising a quantitative measure of a tendency of theindividual to provide at least one type of response of the two or morediffering types of responses to the interference. The processing unit isconfigured to, based at least in part on the computed first decisionboundary metric, adjust the task and/or the interference to derive amodification in the computed at least one decision boundary metric suchthat a further response to the task and/or a further response to theinterference is modified as compared to an earlier response to the taskand/or an earlier response to the interference, thereby indicating amodification of the cognitive response capabilities of the individual.

In another general aspect, a computer-implemented method for enhancingcognitive skills in an individual is provided. The method includesrendering a task with an interference at a user interface; measuringdata indicative of two or more differing types of responses to the taskor to the interference; receiving data indicative of a first response ofan individual to the task and a second response of the individual to theinterference; and analyzing the data indicative of the first responseand the second response to compute at least one response profilerepresentative of the performance of the individual. The method includesdetermining a first decision boundary metric based at least in part onthe at least one response profile, the first decision boundary metriccomprising a quantitative measure of a tendency of the individual toprovide at least one type of response of the two or more differing typesof responses to the interference. The method includes, based at least inpart on the computed first decision boundary metric, adapting the taskand/or the interference to derive a modification in the computed firstdecision boundary metric such that the first response and/or the secondresponse is modified, thereby indicating a modification of the cognitiveresponse capabilities of the individual.

In another general aspect, an apparatus for enhancing cognitive skillsin an individual is provided. The apparatus includes a user interface; amemory to store processor-executable instructions; and a processing unitcommunicatively coupled to the user interface and the memory, in whichupon execution of the processor-executable instructions by theprocessing unit, the processing unit is configured to receive dataindicative of one or more of an amount, concentration, or dose titrationof a pharmaceutical agent, drug, or biologic being or to be administeredto an individual. The processing unit is configured to render a primarytask with an interference at the user interface, one or more of the taskand the interference being time-varying and having a response deadline,such that the user interface imposes a limited time period for receivingat least one type of response from an individual; and the user interfacebeing configured to measure data indicative of two or more differingtypes of responses to the task or to the interference. The processingunit is configured to receive data indicative of a first response of anindividual to the task and a second response of the individual to theinterference, from a first session; analyze the data indicative of thefirst response and the second response to compute a first responseprofile representative of a first performance of the individual; anddetermine a first decision boundary metric based at least in part on theat least one response profile, the first decision boundary metriccomprising a quantitative measure of a tendency of the individual toprovide at least one type of response of the two or more differing typesof responses to the interference. The processing unit is configured to,based at least in part on the computed first decision boundary metricand the amount or concentration of a pharmaceutical agent, drug, orbiologic, adapt the task and/or the interference to generate a secondsession. The processing unit is configured to analyze collected dataindicative of the first response and the second response from the secondsession, to compute a second response profile and a second decisionboundary metric representative of a second performance of theindividual. The processing unit is configured to, based at least in parton the first decision boundary metric and second decision boundarymetric, generate an output to the user interface indicative of at leastone of: (i) a likelihood of the individual experiencing an adverse eventin response to administration of the pharmaceutical agent, drug, orbiologic, (ii) a recommended change in one or more of the amount,concentration, or dose titration of the pharmaceutical agent, drug, orbiologic, (iii) a change in the individual's cognitive responsecapabilities, (iv) a recommended treatment regimen, (v) a recommendationof at least one of a behavioral therapy, counseling, or physicalexercise, or (vi) a degree of effectiveness of at least one of abehavioral therapy, counseling, or physical exercise.

In another general aspect, a computer-implemented method for enhancingcognitive skills in an individual is provided. The method includesreceiving data indicative of one or more of an amount, concentration, ordose titration of a pharmaceutical agent, drug, or biologic being or tobe administered to an individual. The method includes rendering a taskwith an interference at a user interface; measuring data indicative oftwo or more differing types of responses to the task or to theinterference; receiving data indicative of a first response of anindividual to the task and a second response of the individual to theinterference; and analyzing the data indicative of the first responseand the second response to compute a first response profilerepresentative of the performance of the individual. The method includesdetermining a first decision boundary metric based at least in part onthe at least one response profile, the first decision boundary metriccomprising a quantitative measure of a tendency of the individual toprovide at least one type of response of the two or more differing typesof responses to the interference. The method includes, based at least inpart on the computed first decision boundary metric and the amount orconcentration of a pharmaceutical agent, drug, or biologic, adapting thetask and/or the interference such that the at least one response profileis modified. The method includes analyzing the collected data indicativeof the first response and the second response to compute a seconddecision boundary metric representative of a second performance of theindividual. The method includes, based at least in part on the firstdecision boundary metric and second decision boundary metric, generatean output to the user interface indicative of at least one of (i) achange in one or more of the amount, concentration, or dose titration ofthe pharmaceutical agent, drug, or biologic, (ii) a likelihood of theindividual experiencing an adverse event in response to administrationof the pharmaceutical agent, drug, or biologic, (iii) a change in theindividual's cognitive response capabilities, (iv) a recommendedtreatment regimen, (v) a recommendation of at least one of a behavioraltherapy, counseling, or physical exercise, or (vi) a degree ofeffectiveness of at least one of a behavioral therapy, counseling, orphysical exercise.

The details of one or more of the above aspects and implementations areset forth in the accompanying drawings and the description below. Otherfeatures, aspects, and advantages will become apparent from thedescription, the drawings, and the claims.

BRIEF DESCRIPTION OF DRAWINGS

The skilled artisan will understand that the figures, described herein,are for illustration purposes only. It is to be understood that in someinstances various aspects of the described implementations may be shownexaggerated or enlarged to facilitate an understanding of the describedimplementations. In the drawings, like reference characters generallyrefer to like features, functionally similar and/or structurally similarelements throughout the various drawings. The drawings are notnecessarily to scale, emphasis instead being placed upon illustratingthe principles of the teachings. The drawings are not intended to limitthe scope of the present teachings in any way. The system and method maybe better understood from the following illustrative description withreference to the following drawings in which:

FIG. 1 shows a block diagram of an example system, according to theprinciples herein.

FIG. 2 shows a block diagram of an example computing device, accordingto the principles herein.

FIG. 3 shows an example plot of the signal and noise distribution curvescomputed based on an example cognitive test, according to the principlesherein.

FIG. 4A shows an example graphical depiction of a drift-diffusion modelfor linear belief accumulation, according to the principles herein.

FIG. 4B shows an example graphical depiction of a drift-diffusion modelfor non-linear belief accumulation, according to the principles herein.

FIG. 5 shows an example plot of the signal (right curve) and noise basedon an example cognitive platform, according to the principles herein.

FIG. 6 shows an example plot of the conditional probability of aquantifier of belief given a signal, according to the principles herein.

FIGS. 7A-7B show plots of the curves for values of conservative andimpulsive measures, according to the principles herein.

FIGS. 7C-7D show example plots of the formation of belief for linearbelief accumulation and non-linear belief accumulation, respectively,according to the principles herein.

FIGS. 8A-8D show example plots of the probability curves for signaldistribution and noise distribution at the time points shown in FIGS.7A-7D, according to the principles herein.

FIG. 9 shows an example projected two-dimensional (2D) representation ofa three-dimensional (3D) joint distribution, according to the principlesherein.

FIGS. 10A-10D show example user interfaces with instructions to a userthat can be rendered to an example user interface, according to theprinciples herein.

FIGS. 11A-11D show examples of the time-varying features of exampleobjects (targets or non-targets) that can be rendered to an example userinterface, according to the principles herein.

FIGS. 12A-12T show the dynamics of tasks and interferences that can berendered at user interfaces, according to the principles herein.

FIGS. 13A-13D show examples of the dynamics of multi-tasking involvinguser interaction with an implementation of a navigation task and with aninterference rendered to a user interface of an example user interface,according to the principles herein.

FIGS. 14A-14D show examples of the dynamics of an instructions panelrendered to a user interface of an example user interface, according tothe principles herein.

FIGS. 15A-15V show examples of the dynamics of multi-tasking involvinguser interaction with an implementation of a navigation task and with aninterference.

FIGS. 16A-16C show flowcharts of example methods, according to theprinciples herein.

FIG. 17 shows the architecture of an example computer system, accordingto the principles herein.

DETAILED DESCRIPTION

It should be appreciated that all combinations of the concepts discussedin greater detail below (provided such concepts are not mutuallyinconsistent) are contemplated as being part of the inventive subjectmatter disclosed herein. It also should be appreciated that terminologyexplicitly employed herein that also may appear in any disclosureincorporated by reference should be accorded a meaning most consistentwith the particular concepts disclosed herein.

Following below are more detailed descriptions of various conceptsrelated to, and embodiments of, inventive methods, apparatus and systemscomprising a cognitive platform configured for applying signal detectionmetrics in computer-implemented adaptive response-deadline procedures.

It should be appreciated that various concepts introduced above anddiscussed in greater detail below may be implemented in any of numerousways, as the disclosed concepts are not limited to any particular mannerof implementation. Examples of specific implementations and applicationsare provided primarily for illustrative purposes.

As used herein, the term “includes” means includes but is not limitedto, the term “including” means including but not limited to. The term“based on” means based at least in part on.

As used herein, the term “target” refers to a type of stimulus that isspecified to an individual (e.g., in instructions) to be the focus foran interaction. A target differs from a non-target in at least onecharacteristic or feature. Two targets may differ from each other by atleast one characteristic or feature, but overall are still instructed toan individual as a target, in an example where the individual isinstructed/required to make a choice (e.g., between two differentdegrees of a facial expression or other characteristic/featuredifference, such as but not limited to between a happy face and ahappier face or between an angry face and an angrier face).

As used herein, the term “non-target” refers to a type of stimulus thatis not to be the focus for an interaction, whether indicated explicitlyor implicitly to the individual.

As used herein, the term “task” refers to a goal and/or objective to beaccomplished by an individual. The task may require the individual toprovide or withhold a response to a particular stimulus. The “task” canbe configured as a baseline cognitive function that is being measured.

As used herein, the term “interference” refers to a stimulus presentedto the individual such that it interferes with the individual'sperformance of a primary task. In any example herein, an interference isa type of task that is presented/rendered in such a manner that itdiverts or interferes with an individual's attention in performinganother task. In some examples herein, the interference is configured asa secondary task that is presented simultaneously with a primary task,either over a short, discrete time period or over an extended timeperiod (less than the time frame over which the primary task ispresented), or over the entire period of time of the primary task. Inany example herein, the interference can be presented/renderedcontinuously, or continually (i.e., repeated in a certain frequency,irregularly, or somewhat randomly). For example, the interference can bepresented at the end of the primary task or at discrete, interim periodsduring presentation of the primary task. The degree of interference canbe modulated based on the type, amount, and/or temporal length ofpresentation of the interference relative to the primary task.

As used herein, the term “stimulus,” refers to a sensory eventconfigured to evoke a specified functional response from an individual.The degree and type of response can be quantified based on theindividual's interactions with a measuring component (including usingsensor devices or other measuring components). Non-limiting examples ofa stimulus include a navigation path (with an individual beinginstructed to control an avatar or other processor-rendered guide tonavigate the path), or a discrete object, whether a target or anon-target, rendered to a user interface (with an individual beinginstructed to control a computing component to provide input or otherindication relative to the discrete object). In any example herein, thetask and/or interference includes a stimulus.

As used herein, a “trial” includes at least one iteration of renderingof a task and/or interference and at least one receiving of theindividual's response(s) to the task and/or interference. Asnon-limiting examples, a trial can include at least a portion of asingle-tasking task and/or at least a portion of a multi-tasking task.For example, a trial can be a period of time during a navigation task(including a visuo-motor navigation task) in which the individual'sperformance is assessed, such as but not limited to, assessing whetheror the degree of success to which an individual's actions in interactingwith the platform result in a guide (including a computerized avatar)navigating along at least a portion of a certain path or in anenvironment for a time interval (such as but not limited to, fractionsof a second, a second, several seconds, or more) and/or causes the guide(including computerized avatar) to cross (or avoid crossing) performancemilestones along the path or in the environment. In another example, atrial can be a period of time during a targeting task in which theindividual's performance is assessed, such as but not limited to,assessing whether or the degree of success to which an individual'sactions in interacting with the platform result inidentification/selection of a target versus a non-target (e.g., redobject versus yellow object), or discriminates between two differenttypes of targets (a happy face versus a happier face). In theseexamples, the segment of the individual's performance that is designatedas a trial for the navigation task does not need to be co-extensive oraligned with the segment of the individual's performance that isdesignated as a trial for the targeting task.

As used herein, a “session” refers to at least one trial or can includeat least one trial and at least one other type of measurement and/orother user interaction. As a non-limiting example, a session can includeat least one trial and one or more of a measurement using aphysiological or monitoring component and/or a cognitive testingcomponent. As another non-limiting example, a session can include atleast one trial and receipt of data indicative of one or more measuresof an individual's condition, including physiological condition and/orcognitive condition.

In any example herein, an object may be rendered as a depiction of aphysical object (including a polygonal or other object), a face (humanor non-human), or a caricature, other type of object.

In any of the examples herein, instructions can be provided to theindividual to specify how the individual is expected to perform the taskand/or interference in a trial and/or a session. In non-limitingexamples, the instructions can inform the individual of the expectedperformance of a navigation task (e.g., stay on this path, go to theseparts of the environment, cross or avoid certain milestone objects inthe path or environment), a targeting task (e.g., describe or show thetype of object that is the target object versus the non-target object,or describe or show the type of object that is the target object versusthe non-target object, or two different types of target object that theindividual is expected to choose between (e.g., happy face versushappier face)), and/or describe how the individual's performance is tobe scored. In examples, the instructions may be provided visually (e.g.,based on a rendered user interface) or via sound. In various examples,the instructions may be provided once prior to the performance two ormore trials or sessions, or repeated each time prior to the performanceof a trial or a session, or some combination thereof.

While some example systems, methods, and apparatus described herein arebased on an individual being instructed/required to decide/selectbetween a target versus a non-target may, in other exampleimplementations, the example systems, methods, and apparatus can beconfigured such that the individual is instructed/required todecide/choose between two different types of targets (such as but notlimited to between two different degrees of a facial expression or othercharacteristic/feature difference).

In addition, while example systems, methods, and apparatus may bedescribed herein relative to an individual, in other exampleimplementations, the example systems, methods, and apparatus can beconfigured such that two or more individuals, or members of a group(including a clinical population), perform the tasks and/orinterferences, either individually or concurrently.

The instant disclosure is directed to the application of signaldetection metrics such as criterion, bias, and sensitivity indices tocomputer-implemented adaptive time-deadline procedures.

The example systems, methods, and apparatus according to the principlesherein can be implemented, using at least one processing unit of aprogrammed computing device, to characterize the response profiles ofindividuals and groups on the spectrum between impulsive (tend torespond with limited information) or conservative (tend to withholdresponse until maximum information is acquired) in psychophysicalcomputer-implemented adaptive testing procedures.

As described in greater detail below, the computing device can includean application (an “App”) to perform such functionalities as analyzingthe data. For example, the data from the at least one sensor componentcan be analyzed as described herein by a processor executing the App onan example computing device to provide the computed response profile,decision boundary metric (such as but not limited to response criteria),response classifier, and other metrics and analyses described herein.

An example system according to the principles herein provides forgenerating a quantifier of cognitive skills in an individual (using amachine learning response classifier) and/or enhancing cognitive skillsin an individual. In an example implementation, the example systememploys an App running on a mobile communication device or otherhand-held devices. Non-limiting examples of such mobile communicationdevices or hand-held device include a smartphone, such as but notlimited to an iPhone®, a BlackBerry®, or an Android-based smartphone, atablet, a slate, an electronic-reader (e-reader), a digital assistant,or other electronic reader or hand-held, portable, or wearable computingdevice, or any other equivalent device, an Xbox®, a Wii®, or othercomputing system that can be used to render game-like elements. In someexample implementations, the example system can include a head-mounteddevice, such as smart eyeglasses with built-in displays, a smart gogglewith built-in displays, or a smart helmet with built-in displays, andthe user can hold a controller or an input device having one or moresensors in which the controller or the input device communicateswirelessly with the head-mounted device. In some exampleimplementations, the computing system may be stationary, such as adesktop computing system that includes a main computer and a desktopdisplay (or a projector display), in which the user provides inputs tothe App using a keyboard, a computer mouse, a joystick, handheldconsoles, wristbands, or other wearable devices having sensors thatcommunicate with the main computer using wired or wirelesscommunication. In examples herein, the sensors can be configured tomeasure movements of the user's hands, feet, and/or any other part ofthe body. In some example implementations, the example system can beformed as a virtual reality (VR) system (a simulated environmentincluding as an immersive, interactive 3-D experience for a user), anaugmented reality (AR) system (including a live direct or indirect viewof a physical, real-world environment whose elements are augmented bycomputer-generated sensory input such as but not limited to sound,video, graphics and/or GPS data), or a mixed reality (MR) system (alsoreferred to as a hybrid reality which merges the real and virtual worldsto produce new environments and visualizations where physical anddigital objects co-exist and interact substantially in real time).

FIG. 1 shows an example apparatus 100 according to the principles hereinthat can be used to implement the cognitive platform according to theprinciples herein. The example apparatus 100 includes at least onememory 102 and at least one processing unit 104. The at least oneprocessing unit 104 is communicatively coupled to the at least onememory 102.

Example memory 102 can include, but is not limited to, hardware memory,non-transitory tangible media, magnetic storage disks, optical disks,flash drives, computational device memory, random access memory, such asbut not limited to DRAM, SRAM, EDO RAM, any other type of memory, orcombinations thereof. Example processing unit 104 can include, but isnot limited to, a microchip, a processor, a microprocessor, a specialpurpose processor, an application specific integrated circuit, amicrocontroller, a field programmable gate array, any other suitableprocessor, or combinations thereof.

The at least one memory 102 is configured to store processor-executableinstructions 106 and a computing component 108. In a non-limitingexample, the computing component 108 can be used to compute signaldetection metrics in computer-implemented adaptive response-deadlineprocedures. As shown in FIG. 1 , the memory 102 also can be used tostore data 110, such as but not limited to measurement data 112. Invarious examples, the measurement data 112 can include physiologicalmeasurement data of an individual received from a physiologicalcomponent (not shown) and/or data indicative of the response of anindividual to a task and/or an interference rendered at a user interfaceof the apparatus 100 (as described in greater detail below), and/or dataindicative of one or more of an amount, concentration, or dosetitration, or other treatment regimen of a drug, pharmaceutical agent,biologic, or other medication being or to be administered to anindividual.

In a non-limiting example, the at least one processing unit 104 executesthe processor-executable instructions 106 stored in the memory 102 atleast to compute signal detection metrics in computer-implementedadaptive response-deadline procedures using the computing component 108.The at least one processing unit 104 also executes processor-executableinstructions 106 to control a transmission unit to transmit valuesindicative of the computed signal detection metrics and/or controls thememory 102 to store values indicative of the signal detection metrics.

In another non-limiting example, the at least one processing unit 104executes the processor-executable instructions 106 stored in the memory102 at least to apply signal detection metrics in computer-implementedadaptive response-deadline procedures.

In any example herein, the user interface may be a graphical userinterface.

In another non-limiting example, the measurement data 112 can becollected from measurements using one or more physiological ormonitoring components and/or cognitive testing components. In anyexample herein, the one or more physiological components are configuredfor performing physiological measurements. The physiologicalmeasurements provide quantitative measurement data of physiologicalparameters and/or data that can be used for visualization ofphysiological structure and/or functions.

In any example herein, the measurement data 112 can include reactiontime, response variance, correct hits, omission errors, number of falsealarms (such as but not limited to a response to a non-target), learningrate, spatial deviance, subjective ratings, and/or performancethreshold, or data from an analysis, including percent accuracy, hits,and/or misses in the latest completed trial or session. Othernon-limiting examples of measurement data 112 include response time,task completion time, number of tasks completed in a set amount of time,preparation time for task, accuracy of responses, accuracy of responsesunder set conditions (e.g., stimulus difficulty or magnitude level andassociation of multiple stimuli), number of responses a participant canregister in a set time limit number of responses a participant can makewith no time limit, number of attempts at a task needed to complete atask, movement stability, accelerometer and gyroscope data, and/orself-rating.

For a target discrimination task, the cognitive platform may require atemporally-specific and/or a position-specific response from anindividual, including to select between a target and a non-target (e.g.,in a GO/NO-GO task) or to select between two differing types of targets,e.g., in a two-alternative forced choice (2AFC) task (including choosingbetween two differing degrees of a facial expression or othercharacteristic/feature difference). For a navigation task, the cognitiveplatform may require a position-specific and/or a motion-specificresponse from the user. For a facial expression recognition or objectrecognition task, the cognitive platform may require temporally-specificand/or position-specific responses from the user. In non-limitingexamples, the user response to tasks, such as but not limited totargeting and/or navigation and/or facial expression recognition orobject recognition task(s), can be recorded using an input device of thecognitive platform. Non-limiting examples of such input devices caninclude a device for capturing a touch, swipe or other gesture relativeto a user interface, an audio capture device (e.g., a microphone input),or an image capture device (such as but not limited to a touch-screen orother pressure-sensitive or touch-sensitive surface, or a camera),including any form of graphical user interface configured for recordinga user interaction. In other non-limiting examples, the user responserecorded using the cognitive platform for tasks, such as but not limitedto targeting and/or navigation and/or facial expression recognition orobject recognition task(s), can include user actions that cause changesin a position, orientation, or movement of a computing device includingthe cognitive platform. Such changes in a position, orientation, ormovement of a computing device can be recorded using an input devicedisposed in or otherwise coupled to the computing device, such as butnot limited to a sensor. Non-limiting examples of sensors include amotion sensor, position sensor, and/or an image capture device (such asbut not limited to a camera).

In any example herein, the multi-tasking tasks can include anycombination of two or more tasks. The multi-task interactive elements ofan implementation include interactive mechanics configured to engage theindividual in multiple temporally-overlapping tasks, i.e., tasks thatmay require multiple, substantially simultaneous responses from anindividual. In non-limiting examples herein, in an individual'sperformance of at least a portion of a multi-tasking task, the system,method, and apparatus are configured to measure data indicative of theindividual's multiple responses in real-time, and also to measure afirst response from the individual to a task (as a primary task)substantially simultaneously with measuring a second response from theindividual to an interference (as a secondary task).

In an example implementation involving multi-tasking tasks, the computerdevice is configured (such as using at least one specially-programmedprocessing unit) to cause the cognitive platform to present to a usertwo or more different types of tasks, such as but not limited to, targetdiscrimination and/or navigation and/or facial expression recognition orobject recognition tasks, during a short time frame (including inreal-time and/or substantially simultaneously). The computer device isalso configured (such as using at least one specially-programmedprocessing unit) to collect data indicative of the type of user responsereceived for the multi-tasking tasks, within the short time frame(including in real-time and/or substantially simultaneously). In theseexamples, the two or more different types of tasks can be presented tothe individual within the short time frame (including in real-timeand/or substantially simultaneously), and the computing device can beconfigured to receive data indicative of the user response(s) relativeto the two or more different types of tasks within the short time frame(including in real-time and/or substantially simultaneously).

In some examples, the short time frame can be of any time interval at aresolution of up to about 1.0 millisecond or greater. The time intervalscan be, but are not limited to, durations of time of any division of aperiodicity of about 2.0 milliseconds or greater, up to any reasonableend time. The time intervals can be, but are not limited to, about 3.0millisecond, about 5.0 millisecond, about 10 milliseconds, about 25milliseconds, about 40 milliseconds, about 50 milliseconds, about 60milliseconds, about 70 milliseconds, about 100 milliseconds, or greater.In other examples, the short time frame can be, but is not limited to,fractions of a second, about a second, between about 1.0 and about 2.0seconds, or up to about 2.0 seconds, or more.

In any example herein, the cognitive platform can be configured tocollect data indicative of a reaction time of a user's response relativeto the time of presentation of the tasks (including an interference witha task). For example, the computing device can be configured to causethe platform product or cognitive platform to provide smaller or largerreaction time window for a user to provide a response to the tasks as anexample way of adjusting the difficulty level.

In any example herein, the one or more physiological components caninclude any means of measuring physical characteristics of the body andnervous system, including electrical activity, heart rate, blood flow,and oxygenation levels, to provide the measurement data 112. This caninclude camera-based heart rate detection, measurement of galvanic skinresponse, blood pressure measurement, electroencephalogram,electrocardiogram, magnetic resonance imaging, near-infraredspectroscopy, and/or pupil dilation measures, to provide the measurementdata 112. The one or more physiological components can include one ormore sensors for measuring parameter values of the physicalcharacteristics of the body and nervous system, and one or more signalprocessors for processing signals detected by the one or more sensors.

Other examples of physiological measurements to provide measurement data112 include, but are not limited to, the measurement of bodytemperature, heart or other cardiac-related functioning using anelectrocardiograph (ECG), electrical activity using anelectroencephalogram (EEG), event-related potentials (ERPs), functionalmagnetic resonance imaging (fMRI), blood pressure, electrical potentialat a portion of the skin, galvanic skin response (GSR),magneto-encephalogram (MEG), eye-tracking device or other opticaldetection device including processing units programmed to determinedegree of pupillary dilation, functional near-infrared spectroscopy(fNIRS), and/or a positron emission tomography (PET) scanner. AnEEG-fMRI or MEG-fMRI measurement allows for simultaneous acquisition ofelectrophysiology (EEG/MEG) data and hemodynamic (fMRI) data.

The example apparatus of FIG. 1 can be configured as a computing devicefor performing any of the example methods described herein. Thecomputing device can include an App for performing some of thefunctionality of the example methods described herein.

FIG. 2 shows another example apparatus according to the principlesherein, configured as a computing device 200 that can be used toimplement the cognitive platform according to the principles herein. Theexample computing device 200 can include a communication module 210 andan analysis engine 212. The communication module 210 can be implementedto receive data indicative of a response of an individual to the taskand/or a response of the individual to the interference. The analysisengine 212 can be implemented to analyze the data to generate a responseprofile, decision boundary metric (such as but not limited to responsecriteria), a response classifier, and/or other metrics and analysesdescribed herein. As shown in the example of FIG. 2 , the computingdevice 200 can include processor-executable instructions such that aprocessor unit can execute an application (an App) 214 that a user canimplement to initiate the analysis engine 212. In an example, theprocessor-executable instructions can include software, firmware, orother instructions.

The example communication module 210 can be configured to implement anywired and/or wireless communication interface by which information maybe exchanged between the computing device 200 and another computingdevice or computing system. Non-limiting examples of wired communicationinterfaces include, but are not limited to, USB ports, RS232 connectors,RJ45 connectors, and Ethernet connectors, and any appropriate circuitryassociated therewith. Non-limiting examples of wireless communicationinterfaces may include, but are not limited to, interfaces implementingBluetooth® technology, Wi-Fi, Wi-Max, IEEE 802.11 technology, radiofrequency (RF) communications, Infrared Data Association (IrDA)compatible protocols, Local Area Networks (LAN), Wide Area Networks(WAN), and Shared Wireless Access Protocol (SWAP).

In an example implementation, the example computing device 200 includesat least one other component that is configured to transmit a signalfrom the apparatus to a second computing device. For example, the atleast one component can include a transmitter or a transceiverconfigured to transmit a signal including data indicative of ameasurement by at least one sensor component to the second computingdevice.

In any example herein, the App 214 on the computing device 200 caninclude processor-executable instructions such that a processor unit ofthe computing device implements an analysis engine to analyze dataindicative of the individual's response to the rendered tasks and/orinterference to provide a response profile, decision boundary metric(such as but not limited to response criteria), a response classifier,and other metrics and analyses described herein. In some example, theApp 214 can include processor-executable instructions to provide one ormore of: (i) a classifier output indicative of the cognitive responsecapabilities of the individual, (ii) a likelihood of the individualexperiencing an adverse event in response to administration of thepharmaceutical agent, drug, or biologic, (iii) a change in one or moreof the amount, concentration, or dose titration of the pharmaceuticalagent, drug, or biologic, and (iv) a change in the individual'scognitive response capabilities, a recommended treatment regimen, orrecommending or determining a degree of effectiveness of at least one ofa behavioral therapy, counseling, or physical exercise.

In any example herein, the App 214 can be configured to receivemeasurement data including physiological measurement data of anindividual received from a physiological component, and/or dataindicative of the response of an individual to a task and/or aninterference rendered at a user interface of the apparatus 100 (asdescribed in greater detail below), and/or data indicative of one ormore of an amount, concentration, or dose titration, or other treatmentregimen of a drug, pharmaceutical agent, biologic, or other medicationbeing or to be administered to an individual.

Non-limiting examples of the computing device include a smartphone, atablet, a slate, an e-reader, a digital assistant, or any otherequivalent device, including any of the mobile communication devicesdescribed hereinabove. As an example, the computing device can include aprocessor unit that is configured to execute an application thatincludes an analysis module for analyzing the data indicative of theindividual's response to the rendered tasks and/or interference.

The example systems, methods, and apparatus can be implemented as acomponent in a product comprising a computing device that usescomputer-implemented adaptive psychophysical procedures to assess humanperformance or delivers psychological/perceptual therapy.

A non-limiting example characteristic of a type of decision boundarymetric that can be computed based on the response profile is theresponse criterion (a time-point measure), calculated using the standardprocedure to calculate response criterion for a signal detectionpsychophysics assessment. See, e.g., Macmillan and Creelman (2004),“Signal Detection: A Users Guide” 2^(nd) edition, Lawrence Erlbaum USA.

In other non-limiting examples, the decision boundary metric may be morethan a single quantitative measure but rather a curve defined byquantitative parameters based on which decision boundary metrics can becomputed, such as but not limited to an area to one side or the other ofthe response profile curve. Other non-limiting example types of decisionboundary metrics that can be computed to characterize the decisionboundary curves for evaluating the time-varying characteristics of thedecision process include a distance between the initial bias point (thestarting point of the belief accumulation trajectory) and the criterion,a distance to the decision boundary, a “waiting cost” (e.g., thedistance from the initial decision boundary and the maximum decisionboundary, or the total area of the curve to that point), or the areabetween the decision boundary and the criterion line (including the areanormalized to the response deadline to yield a measure of an “averagedecision boundary” or an “average criterion”).

While examples herein may be described based on computation of aresponse criterion, other types of decision boundary metrics areapplicable.

FIG. 3 shows an example plot of the signal (right curve 302) and noise(left curve 304) distributions of an individual or group psychophysicaldata, and the computed response criterion, based on data collected fromindividuals that performed a Test of Variables of Attention (TOVA®) test(The TOVA Company, Los Alamitos, CA). The TOVA® test is an example of acomputerized test that can be used by a healthcare professional as anaid in an assessment of an individual's attention deficits andimpulsivity, including attention-deficit/hyperactivity disorder (ADHD).

In FIG. 3 , the vertical line represents the response criterion 300. Theintercept of the criterion line on the X axis (in Z units) can be usedto provide an indication of the tendency of an individual to respond‘yes’ (further right) or ‘no’ (further left) from a point of zero bias(ρ). As indicated in FIG. 3 , ρ is located on the x-axis where thesignal distribution (right curve 302) and the noise distribution (leftcurve 304) intersect. Response criterion intercepts left of p mayindicate an individual's overall tendency to a more impulsive strategyand intercepts right of ρ may indicate an individual's overall tendencyto a more conservative strategy. Response criterion intercepts at pindicate a balanced strategy.

The example systems, methods, and apparatus can be configured toimplement a further extension of signal detection theory to atime-limited task (as described in greater detail below). The examplesystems, methods, and apparatus can be configured to extend accumulationof belief information, modeled using a computational model of humandecision-making (such as but not limited to a drift-diffusion model(DDM) and/or a Bayesian model), and decision boundaries that reflectdifferent strategies.

Following is a description of a non-limiting example use of acomputational model of human decision-making (based on a drift diffusionmodel). While the drift diffusion model is used as the example, othertypes of models apply, including a Bayesian model. The drift-diffusionmodel (DDM) can be applied for systems with two-choice decision making.See, e.g., Ratcliff, R. (1978), “A theory of memory retrieval.”Psychological Review, 85, 59-108; Ratcliff, R., & Tuerlinckx, F. (2002),“Estimating parameters of the diffusion model: Approaches to dealingwith contaminant reaction times and parameter variability,” PsychonomicBulletin & Review, 9, 438-481. The diffusion model is based on anassumption that binary decision processes are driven by systematic andrandom influences.

FIG. 4A shows an example plot of the diffusion model with a stimulusthat results in a linear drift rate, showing example paths of theaccumulation of belief from a stimulus. It shows the distributions ofdrift rates across trials for targets (signal) and non-targets (noise).The vertical line is the response criterion. The drift rate on eachtrial is determined by the distance between the drift criterion and asample from the drift distribution. The process starts at point x, andmoves over time until it reaches the lower threshold at “A” or the upperthreshold at “B”. The DDM assumes that an individual is accumulatingevidence for one or other of the alternative thresholds at each timestep, and integrating that evidence to develop a belief, until adecision threshold is reached. Depending on which threshold is reached,different responses (i.e., Response A or Response B) are initiated bythe individual. In a psychological application, this means that thedecision process is finished and the response system is being activated,in which the individual initiates the corresponding response. Asdescribed in non-limiting examples below, this can require a physicalaction of the individual to actuate a component of the system orapparatus to provide the response (such as but not limited to tapping onthe user interface in response to a target). The systematic influencesare called the drift rate, and they drive the process in a givendirection. The random influences add an erratic fluctuation to theconstant path. With a given set of parameters, the model predictsdistributions of process durations (i.e., response times) for the twopossible outcomes of the process.

FIG. 4A also shows an example drift-diffusion path of the process,illustrating that the path is not straight but rather oscillates betweenthe two boundaries, due to random influences. In a situation in whichindividuals are required to categorize stimuli, the process describesthe ratio of information gathered over time that causes an individual tofoster each of the two possible stimulus interpretations. Once beliefpoints with sufficient clarity is reached, the individual initiates aresponse. In the example of FIG. 4A, processes reaching the upperthreshold are indicative of a positive drift rate. In some trials, therandom influences can outweigh the drift, and the process terminates atthe lower threshold.

Example parameters of the drift diffusion model include quantifiers ofthe thresholds (“A” or “B”), the starting point (x), the drift rate, anda response time constant (to). The DDM can provide a measure ofconservatism, an indication that the process takes more time to reachone threshold and that it will reach the other threshold (opposite tothe drift) less frequently. The starting point (x) provides an indicatorof bias (reflecting differences in the amount of information that isrequired before the alternative responses are initiated). If x is closerto “A”, an individual requires a smaller (relative) amount ofinformation to develop a belief to execute Response A, as compared witha larger (relative) amount of information that the individual would needto execute Response B. The smaller the distance between the startingpoint (x) and a threshold, the shorter the process durations would befor the individual to execute the corresponding response. A positivevalue of drift rate (v) serves as a measure of the mean rate of approachto the upper threshold (“A”). The drift rate indicates the relativeamount of information per time unit that the individual absorbsinformation on a stimulus to develop a belief in order to initiate andexecute a response. In an example, comparison of the drift ratescomputed from data of one individual to data from another can provide ameasure of relative perceptual sensitivity of the individuals. Inanother example, comparison of the drift rates can provide a relativemeasure of task difficulty. For computation of the response time, theDDM allows for estimating their total duration, and the response timeconstant (to) indicates the duration of extra-decisional processes. TheDDM has been shown to describe accuracy and reaction times in human datafor tasks. In the non-limiting example of FIG. 4A, the total responsetime is computed as a sum of the magnitude of time for stimulus encoding(t_(S)), the time the individual takes for the decision, and the timefor response execution.

As compared to the traditional drift diffusion model that is based onstimuli that result in linear drift rates, the example systems, methods,and apparatus according to the principles herein are configured torender stimuli that result in non-linear drift rates, which stimuli arebased on tasks and/or interferences that are time-varying and havespecified response deadlines. As a result, the example systems, methods,and apparatus according to the principles herein are configured to applya modified diffusion model (modified DDM) based on these stimuli thatresult in non-linear drift rates.

FIG. 4B shows an example plot of a non-linear drift rate in a driftdiffusion computation. Example parameters of the modified DDM alsoinclude quantifiers of the thresholds (“A” or “B”), the starting point(x), the drift rate, and a response time constant (t₀). Based on datacollected from user interaction with the example systems, methods, andapparatus herein, the systems, methods, and apparatus are configured toapply the modified DDM with the non-linear drift rates to provide ameasure of the conservatism or impulsivity of the strategy employed inthe user interaction with the example platforms herein. The examplesystems, methods, and apparatus are configured to compute a measure ofthe conservatism or impulsivity of the strategy used by an individualbased on the modified DDM model, to provide an indication of the timethe process takes for a given individual to reach one threshold and ascompared to reaching the other threshold (opposite to the drift). Thestarting point (x) in FIG. 4B also provides an indicator of bias(reflecting differences in the amount of information that is requiredbefore the alternative responses are initiated). For computation of theresponse time, the DDM allows for estimating their total duration, andthe response time constant (to) indicates the duration ofextra-decisional processes.

In the example systems, methods, and apparatus according to theprinciples herein, the non-linear drift rate results from thetime-varying nature of the stimuli, including (i) the time-varyingfeature of portions of the task and/or interference rendered to the userinterface for user response (as a result of which the amount ofinformation available for an individual to develop a belief is presentedin a temporally non-linear manner), and (ii) the time limit of theresponse deadlines of the task and/or interference, which can influencean individual's sense of timing to develop a belief in order to initiatea response. In this example as well, a positive value of drift rate (v)serves as a measure of the mean rate of approach to the upper threshold(“A”). The non-linear drift rate indicates the relative amount ofinformation per time unit that the individual absorbs to develop abelief in order to initiate and execute a response. In an example,comparison of the drift rate computed from response data collected fromone individual to the drift rate computed from response data collectedfrom another individual can be used to provide a measure of relativeperceptual sensitivity of the individuals. In another example,comparison of the drift rate computed from response data collected froma given individual from two or more different interaction sessions canbe used to provide a relative measure of task difficulty. Forcomputation of the response time of the individual's responses, themodified DDM also allows for estimating the total duration of theresponse time, and the response time constant (to) indicates theduration of extra-decisional processes. In the non-limiting example ofFIG. 4A, the total response time is computed as a sum of the magnitudeof time for stimulus encoding (t_(S)), the time the individual takes forthe decision, and the time for response execution.

For the modified DDM, the distance between the thresholds (i.e., between“A” and “B”) provides a measure of conservatism—that is, the larger theseparation, the more information is collected prior to an individualexecuting a response. The starting point (x) also provides an estimateof relative conservatism: if the process starts above or below themidpoint between the two thresholds, different amounts of informationare required for both responses; that is, a more conservative decisioncriterion is applied for one response, and a more liberal criterion(i.e., impulsive) for the opposite response. The drift rate (v)indicates the (relative) amount of information gathered per time,denoting either perceptual sensitivity or task difficulty.

FIG. 5 shows an example plot of the signal (right curve 502) and noise(left curve 504) distributions of an individual or group psychophysicaldata, and the computed response criterion 500, based on data collectedfrom an individual's responses with the tasks and/or interferencerendered at a user interface of a computing device according to theprinciples herein (as described in greater detail hereinbelow). Theintercept of the criterion line on the X axis (in Z units) can be usedto provide an indication of the tendency of an individual to respond‘yes’ (further right) or ‘no’ (further left). The response criterion 500is left of the zero-bias decision point (ρ) and where the signal andnoise distributions intersect. In the non-limiting example of FIG. 5 , ρis the location of the zero-bias decision on the decision axis inZ-units, and response criterion values to the left of p indicate animpulsive strategy and response criterion values to the right of pindicate a conservative strategy, with intercepts on the zero-bias pointindicating a balanced strategy.

The example systems, methods, and apparatus according to the principlesherein can be configured to compute a response criterion based on thedetection or classification task(s) described herein that are composedof signal and non-signal response targets (as stimuli), in which a userindicates a response that indicates a feature, or multiple features, arepresent in a series of sequential presentations of stimuli orsimultaneous presentation of stimuli.

The data indicative of the results of the classification of anindividual according to the principles herein (including a classifieroutput) can be transmitted (with the pertinent consent) as a signal toone or more of a medical device, healthcare computing system, or otherdevice, and/or to a medical practitioner, a health practitioner, aphysical therapist, a behavioral therapist, a sports medicinepractitioner, a pharmacist, or other practitioner, to allow formulationof a course of treatment for the individual or to modify an existingcourse of treatment, including to determine a change in one or more ofan amount, concentration, or dose titration of a drug, biologic or otherpharmaceutical agent being or to be administered to the individualand/or to determine an optimal type or combination of drug, biologic orother pharmaceutical agent to be administered to the individual.

The example systems, methods, and apparatus herein provide computerizedclassifiers, treatment tools, and other tools that can be used by amedical, behavioral, healthcare, or other professional as an aid in anassessment and/or enhancement of an individual's attention, workingmemory, and goal management. In an example implementation, the examplesystems, methods, and apparatus herein apply the modified DDM to thecollected data to provide measures of conservatism or impulsivity. Theexample analysis performed using the example systems, methods, andapparatus according to the principles herein can be used to providemeasures of attention deficits and impulsivity (including ADHD). Theexample systems, methods, and apparatus herein provide computerizedclassifiers, treatment tools, and other tools that can be used as aidsin assessment and/or enhancement in other cognitive domains, such as butnot limited to attention, memory, motor, reaction, executive function,decision-making, problem-solving, language processing, andcomprehension. In some examples, the systems, methods, and apparatus canbe used to compute measures for use for cognitive monitoring and/ordisease monitoring. In some examples, the systems, methods, andapparatus can be used to compute measures for use for cognitivemonitoring and/or disease monitoring during treatment of one or morecognitive conditions and/or diseases and/or executive functiondisorders.

FIG. 6 shows an example plot of the conditional probability of aquantifier of belief given a signal (P (Belief|Signal)) along thez-axis, Time as the x-axis, and the quantifier of belief is the y-axis.The curve labeled Valid Target and the curve labeled Invalid Target(each lying in the x-y plane) indicate data values quantifying belieftrajectories of accumulated (noisy) information over time for a user todevelop a strong belief one way or another as to the appropriateresponse. The four curves labeled Signal and four curves labeled Noiseeach has a magnitude in the z-direction and are data values of the“signal” distribution and “noise” distribution at different points intime. Each signal curve is paired with a noise, and the pair istime-displaced along the x-axis (at times t=t₀, t₁, t₂, t₃). As shown inFIG. 6 , each signal-noise curve pair spreads out (i.e., becomes a widercurve as time increases from t₀ to t₃), to represent the probability ofa given degree of belief at a given point in time given a type ofsignal. In this time-evolving model, the decision is made when thebelief trajectory crosses a decision boundary. FIG. 6 also shows examplecurves that serve as projected decision boundaries for response datavalues indicative of an impulsive strategy (narrower curve in the x-yplane) and response data values indicative of a conservative strategy(wider curve in the x-y plane). As described herein, the impulsivestrategy requires much less extreme belief (i.e., less extreme values ofthe quantifier of belief) in order to arrive at a decision. As alsodescribed herein, the conservative strategy requires much more extremebelief (i.e., more extreme values of the quantifier of belief) in orderto arrive at a decision. As the perceived response deadline approaches,these decision boundaries converge on the criterion value described insignal detection theory.

An example system, method or apparatus according to the principlesherein can be applied to data values as indicated in accordance withFIG. 6 to compute a classifier to apply to data indicative of a user'sresponses to the tasks and/or interference rendered at a user interfaceto determine a measure of whether an individual is employing a moreconservative strategy or a more impulsive strategy.

Such an example model, e.g., as described in connection with FIG. 6 ,enables Bayesian inference of the shape of an individual's decisionboundary based on the response times and correctness of a sequence ofdecisions. In a non-limiting example, a metric can be derivedcharacterizing a degree of impulsiveness of the individual's responsestrategy based on the area of this decision boundary compared with thearea of the “ideal” decision boundary (the response deadline times thefull width of the belief axis).

FIGS. 7A-7B show example plots of the curves for values of conservativeand impulsive measures from the trial start (t=0) to the perceivedresponse deadline (R-DP). FIG. 7A shows example curves for atwo-alternative forced choice (2AFC) task, where an individual isinstructed/required to discriminate between two types of stimulus (suchas but not limited to targets with differing degrees of a facialexpression or other characteristic/feature difference), hence both areultimately targets as they require an action/response from theindividual. FIG. 7B shows example curves for a GO/NO-GO task, where theindividual is instructed/required to decide whether a stimulus is atarget requiring response/action (based on the instructions) or anon-target requiring inaction/no response (based on the instructions).In some examples herein, the stimuli are designated as GO/NO-GO task(i.e., with instructions to act/give response for a target or notact/give no response). In FIG. 7A, the plot shows the curves versusdevelopment of belief (for two types of target stimuli at various timepoints (t=0, a, b, c, d) as well as the decision boundaries relative tothe value of the response criterion for the time-varying stimulidescribed herein. FIG. 7B shows the differing types of values and shapesof conservative and impulsive measures from the trial start to aresponse deadline for the traditional GO/NO GO task (target vs.non-target), a pass/fail or yes/no type of test that has two boundaryconditions or a binary classification. As shown in FIG. 7B, the curvesfor the values of the conservative and impulsive measures for the GO/NOGO task does not have a right-side decision boundary because waiting toact/response is not a momentary decision that an individual arrive at,rather it is a process that continues until the end of the trial (or atleast until the attention of the individual is allocated elsewhere).

FIGS. 7C-7D show example plots of the formation of belief for linearbelief accumulation and non-linear belief accumulation, respectively. Ina system with linear belief accumulation, FIG. 7C shows the values ofthe mean belief for targets (M_(B)(targets)) and mean belief fornon-targets (M_(B)(non-targets)) versus the development of belief (fortarget versus non-target) at various time points (t=0, a, b, c, d)relative to the value of the response criterion. FIG. 7C also shows thetarget confidence interval and non-target confidence interval for thelinear belief accumulation. In a system with non-linear beliefaccumulation, FIG. 7D shows the values of the mean belief for targets(M_(B)(targets)) and mean belief for non-targets (M_(B)(non-targets))versus development of belief (for target versus non-target) at varioustime points (t=0, a, b, c, d) relative to the value of the responsecriterion for the nonlinear belief accumulation. FIG. 7C also shows thetarget confidence interval and non-target confidence interval. Atraditional GO/NO GO task involves presentation to an individual for aspecific period of time of a stimulus without a time-varying aspect, andsupports linear accumulation of belief from the information available tothe individual for developing belief. By contrast, the example tasksand/or interferences according to the principles herein have at leastone time-varying feature (based on their feature dynamics), resulting innonlinear belief accumulation.

FIGS. 8A-8D show plots of the probability curves for the “signal”distribution and the “noise” distribution at the different points intime (t=a, b, c, d) shown in FIGS. 7A-7D. Each of FIGS. 8A-8D shows asignal curve and a noise curve at differing time-points displaced alongthe x-axis (similar to the signal and noise curves shown at time-pointst=t₀, t₁, t₂, t₃ in FIG. 6 ). As shown in FIGS. 8A-8D, the signal-noisecurve pair spreads out (i.e., becomes a wider curve) as time increasesfrom t=a to t=d, representing the probability of a given degree ofbelief at a given point in time for a given type of signal. In thistime-evolving model, the decision is made when the belief trajectorycrosses a decision boundary. FIGS. 8A-8D also show the values of themean belief for targets (M_(B)(targets)) and mean belief for non-targets(M_(B)(non-targets)) versus the development of belief. In FIG. 8D, thedecision boundaries (conservative and impulsive) are converged at thecriterion.

An example system, method, and apparatus according to the principlesherein can be configured to execute an example response classifier togenerate a quantifier of the cognitive skills in an individual. Theexample response classifier can be built using a machine learning tool,such as but not limited to linear/logistic regression, principalcomponent analysis, generalized linear mixed models, random decisionforests, support vector machines, and/or artificial neural networks. Ina non-limiting example, classification techniques that may be used totrain a classifier using the performance measures of a labeledpopulation of individuals (e.g., individuals with known cognitivedisorders, executive function disorder, disease or other cognitivecondition). The trained classifier can be applied to measures of theresponses of the individual to the tasks and/or interference to classifythe individual as to a population label (e.g., cognitive disorder,executive function disorder, disease or other cognitive condition). Inan example, machine learning may be implemented using cluster analysis.Each measurement of the cognitive response capabilities of participatingindividuals can be used as the parameter that groups the individuals tosubsets or clusters. For example, the subset or cluster labels may be adiagnosis of a cognitive disorder, cognitive disorder, executivefunction disorder, disease or other cognitive condition. Using a clusteranalysis, similarity metric of each subset and the separation betweendifferent subsets can be computed, and these similarity metrics may beapplied to data indicative of an individual's responses to a task and/orinterference to classify that individual to a subset. In anotherexample, the classifier may be a supervised machine learning tool basedon artificial neural networks. In such a case, the performance measuresof individuals with known cognitive abilities may be used to train theneural network algorithm to model the complex relationships among thedifferent performance measures. A trained classifier can be applied tothe performance/response measures of a given individual to generate aclassifier output indicative of the cognitive response capabilities ofthe individual. Other applicable techniques for generating a classifierinclude a regression or Monte Carlo technique for projecting cognitiveabilities based on his/her cognitive performance. The classifier may bebuilt using other data, including a physiological measure (e.g., EEG)and demographic measures.

In an example implementation, a programmed processing unit is configuredto execute processor-executable instructions to render a task with aninterference at a user interface. As described in greater detail herein,one or more of the task and the interference can be time-varying andhave a response deadline, such that the user interface imposes a limitedtime period for receiving at least one type of response from theindividual interacting with the apparatus or system. The processing unitis configured to control the user interface to measure data indicativeof two or more differing types of responses to the task or to theinterference. The programmed processing unit is further configured toexecute processor-executable instructions to cause the example system orapparatus to receive data indicative of a first response of theindividual to the task and a second response of the individual to theinterference, analyze at least some portion of the data to compute atleast one response profile representative of the performance of theindividual, and determine a decision boundary metric (such as but notlimited to the response criterion) from the response profile. Asdescribed herein, including in connection with FIGS. 4A and 4B, thedecision boundary metric (such as but not limited to the responsecriterion) gives a quantitative measure of a tendency of the individualto provide at least one type of response of the two or more differingtypes of responses (Response A vs. Response B) to the task or theinterference. The programmed processing unit is further configured toexecute processor-executable instructions to execute a responseclassifier based on the computed values of the decision boundary metric(such as but not limited to the response criterion), to generate aclassifier output indicative of the cognitive response capabilities ofthe individual.

In an example, the processing unit further uses the classifier outputfor one or more of changing one or more of the amount, concentration, ordose titration of the pharmaceutical agent, drug, biologic or othermedication, identifying a likelihood of the individual experiencing anadverse event in response to administration of the pharmaceutical agent,drug, biologic or other medication, identifying a change in theindividual's cognitive response capabilities, recommending a treatmentregimen, or recommending or determining a degree of effectiveness of atleast one of a behavioral therapy, counseling, or physical exercise.

In any example herein, the example response classifier can be used as anintelligent proxy for quantifiable assessments of an individual'scognitive abilities. That is, once a response classifier is trained, theclassifier output can be used to provide the indication of the cognitiveresponse capabilities of multiple individuals without use of othercognitive or behavioral assessment tests.

Monitoring cognitive deficits allows individuals, and/or medical,healthcare, behavioral, or other professional (with consent) to monitorthe status or progression of a cognitive condition, a disease, or anexecutive function disorder. For example, individuals with Alzheimer'sdisease may shows mild symptoms initially, but others have moredebilitating symptoms. If the status or progression of the cognitivesymptoms can be regularly or periodically quantified, it can provide anindication of when a form of pharmaceutical agent or other drug may beadministered or to indicate when quality of life might be compromised(such as the need for assisted living). Monitoring cognitive deficitsalso allows individuals, and/or medical, healthcare, behavioral, orother professional (with consent) to monitor the response of theindividual to any treatment or intervention, particularly in cases wherethe intervention is known to be selectively effective for certainindividuals. In an example, a cognitive assessment tool based on theclassifiers herein can be an individual patient with attention deficithyperactivity disorder (ADHD). In another example, the classifiers andother tools herein can be used as a monitor of the presence and/orseverity of any cognitive side effects from therapies with knowncognitive impact, such as but not limited to chemotherapy, or thatinvolve uncharacterized or poorly characterized pharmacodynamics. In anyexample herein, the cognitive performance measurements and/or classifieranalysis of the data may be performed every 30 minutes, each few hours,daily, two or more times per week, weekly, bi-weekly, each month, oronce per year.

In an example, response classifier can be used as an intelligent proxyfor quantifiable measures of the degree of conservativeness orimpulsivity of the individual.

In an example, the analysis of the data indicative of the first responseand/or the second response generates a first response profile that is animpulsive response profile or a conservative response profile.

In a non-limiting example, the task and the interference can be renderedat the user interface such that the individual is required to providethe first response and the second response within a limited period oftime. In an example, the individual is required to provide the firstresponse and the second response substantially simultaneously.

In the example herein, “substantially simultaneously” means tasks arerendered, or response measurements are performed, within less than about5 milliseconds of each other, or within about 10 milliseconds, about 20milliseconds, about 50 milliseconds, about 75 milliseconds, about 100milliseconds, or about 150 milliseconds or less, about 200 millisecondsor less, about 250 milliseconds or less, of each other. In any exampleherein, “substantially simultaneously” is a period of time less than theaverage human reaction time. In another example, two tasks may besubstantially simultaneous if the individual switches between the twotasks within a pre-set amount of time. The set amount of time forswitching considered “substantially simultaneously” can be about 1 tenthof a second, 1 second, about 5 seconds, about 10 seconds, about 30seconds, or greater.

In a non-limiting example, the classifier output can be indicative ofthe degree of impulsiveness or conservativeness of the individual'scognitive response capabilities.

In an example, the processing unit executes further instructionsincluding applying at least one adaptive procedure to modify the taskand/or the interference, such that analysis of the data indicative ofthe first response and/or the second response indicates a modificationof the first response profile.

In an example, the at least one response profile changes from animpulsive response profile to a conservative response profile based onreceived data collected from measurement of the first response and/orthe second response to the modified task and/or the modified.

In an example, the task or the interference includes a response-deadlineprocedure having the response-deadline; and wherein the at least oneadaptive procedure modifies the response-deadline to modify aperformance characteristics of the individual to an impulsive responseprofile or a conservative response profile.

In an example, the processing unit controls the user interface to modifya temporal length of the response window associated with theresponse-deadline procedure.

In an example, the processing unit controls the user interface to modifya time-varying characteristics of an aspect of the task or theinterference rendered to the user interface.

As described in connection with FIGS. 4A and 4B, the time-varyingcharacteristics of the task and/or interference results in thetime-varying availability of information about the target, such thatthat a linear drift-rate is no longer sufficient to capture developmentof belief over time (rather, requiring a nonlinear drift rate). Atime-varying characteristic can be a feature such as, but not limitedto, color, shape, type of creature, facial expression, or other featurethat an individual requires in order to discriminate between a targetand a non-target, resulting in differing time-characteristics ofavailability. The trial-by-trial adjustment of the response windowlength also can be a time-varying characteristic that alters theindividual's perception of where the decision criteria needs to be inorder to respond successfully to a task and/or an interference. Anothertime-varying characteristic that can be modified is the degree that aninterference interferes with a parallel task which can introduceinterruptions in belief accumulation and/or response selection andexecution.

In an example, modifying the time-varying characteristics of an aspectof the task or the interference includes adjusting a temporal length ofthe rendering of the task or interference at the user interface betweentwo or more sessions of interactions of the individual.

In an example, the time-varying characteristics is one or more of aspeed of an object, a rate of change of a facial expression, a directionof trajectory of an object, a change of orientation of an object, atleast one color of an object, a type of an object, or a size of anobject.

In an example, the change in type of object is effected using morphingfrom a first type of object to a second type of object or rendering ablendshape as a proportionate combination of the first type of objectand the second type of object.

In a non-limiting example, the processing unit can be configured torender a user interface or cause another component to execute least oneelement for indicating a reward to the individual for a degree ofsuccess in interacting with a task and/or interference, or anotherfeature or other element of a system or apparatus. A reward computerelement can be a computer generated feature that is delivered to a userto promote user satisfaction with the example system, method orapparatus, and as a result, increase positive user interaction and henceenjoyment of the experience of the individual.

In an example, the processing unit further computes as the classifieroutput parameters indicative of one or more of a bias sensitivityderived from the data indicative of the first response and the secondresponse, a non-decision time sensitivity to parallel tasks, a beliefaccumulation sensitivity to parallel task demands, a reward ratesensitivity, or a response window estimation efficiency. Biassensitivity can be a measure of how sensitive an individual is tocertain of the tasks based on their bias (tendency to one type ofresponse versus another (e.g., Response A vs. Response B)). Non-decisiontime sensitivity to parallel tasks can be a measure of how much theinterference interferes with the individual's performance of the primarytask. Belief accumulation sensitivity to parallel task demands can be ameasure of the rate of the individual to develop/accumulate belief forresponding to the interference during the individual's performance ofthe primary task. Reward rate sensitivity can be used to measure how anindividual's response changes based on the temporal length of theresponse deadline window. When near the end of a response deadlinewindow (e.g., as individual sees interference about to move off thefield of view), the individual realizes that he is running out of timeto make a decision. This measures how the individual's responses changeaccordingly. Response window estimation efficiency is explained asfollows. When the individual is making a decision to act/respond or notact/no response, the decision needs to be based on when the individualthinks his time to respond is running out. For a varying window, theindividual will not be able to measure that window perfectly, but withenough trials/session, based the response data, it may be possible toinfer how good the individual is at making that estimation based on thetime-varying aspect (e.g., trajectory) of the objects in the task orinterference.

An example system, method, and apparatus according to the principlesherein can be configured to train a classifier model of a measure of thecognitive capabilities of individuals based on feedback data from theoutput of the computational model of human decision-making forindividuals that are previously classified as to the measure ofcognitive abilities of interest. For example, the response classifiercan be trained using a plurality of training datasets, where eachtraining dataset is associated with a previously classified individualfrom a group of individuals. Each of the training dataset includes dataindicative of the first response of the classified individual to thetask and data indicative of the second response of the classifiedindividual to the interference, based on the classified individual'sinteraction with an example apparatus, system, or computing devicedescribed herein. The example response classifier also can take as inputdata indicative of the performance of the classified individual at acognitive test, and/or a behavioral test, and/or data indicative of adiagnosis of a status or progression of a cognitive condition, adisease, or a disorder (including an executive function disorder) of theclassified individual.

In any example herein, the at least one processing unit can beprogrammed to cause an actuating component of the apparatus (includingthe cognitive platform) to effect auditory, tactile, or vibrationalcomputerized elements to effect the stimulus or other interaction withthe individual. In a non-limiting example, the at least one processingunit can be programmed to cause a component of the cognitive platform toreceive data indicative of at least one response from the individualbased on the user interaction with the task and/or interference,including responses provided using an input device. In an example whereat least one graphical user interface is rendered to present thecomputerized stimulus to the individual, the at least one processingunit can be programmed to cause the graphical user interface to receivethe data indicative of at least one response from the individual.

In any example herein, the data indicative of the response of theindividual to a task and/or an interference can be measured using atleast one sensor device contained in and/or coupled to an example systemor apparatus herein, such as but not limited to a gyroscope, anaccelerometer, a motion sensor, a position sensor, a pressure sensor, anoptical sensor, an auditory sensor, a vibrational sensor, a videocamera, a pressure-sensitive surface, a touch-sensitive surface, orother type of sensor. In other examples, the data indicative of theresponse of the individual to the task and/or an interference can bemeasured using other types of sensor devices, including a video camera,a microphone, joystick, keyboard, a mouse, a treadmill, elliptical,bicycle, steppers, or a gaming system (including a Wii®, a Playstation®,or an Xbox® or other gaming system). The data can be generated based onphysical actions of the individual that are detected and/or measuredusing the at least one sensor device, as the individual executed aresponse to the stimuli presented with the task and/or interference.

The user may respond to tasks by interacting with the computer device.In an example, the user may execute a response using a keyboard foralpha-numeric or directional inputs; a mouse for go/no go clicking,screen location inputs, and movement inputs; a joystick for movementinputs, screen location inputs, and clicking inputs; a microphone foraudio inputs; a camera for still or motion optical inputs; sensors suchas accelerometer and gyroscopes for device movement inputs; amongothers. Non-limiting example inputs for a game system include but arenot limited to a game controller for navigation and clicking inputs, agame controller with accelerometer and gyroscope inputs, and a camerafor motion optical inputs. Example inputs for a mobile device or tabletinclude a touch screen for screen location information inputs, virtualkeyboard alpha-numeric inputs, go/no go tapping inputs, and touch screenmovement inputs; accelerometer and gyroscope motion inputs; a microphonefor audio inputs; and a camera for still or motion optical inputs, amongothers. In other examples, data indicative of the individual's responsecan include physiological sensors/measures to incorporate inputs fromthe user's physical state, such as but not limited toelectroencephalogram (EEG), magnetoencephalography (MEG), heart rate,heart rate variability, blood pressure, weight, eye movements, pupildilation, electrodermal responses such as the galvanic skin response,blood glucose level, respiratory rate, and blood oxygenation.

In any example herein, the individual may be instructed to provide aresponse via a physical action of clicking a button and/or moving acursor to a correct location on a screen, head movement, finger or handmovement, vocal response, eye movement, or other action of theindividual.

As a non-limiting example, an individual's response to a task orinterference rendered at the user interface that requires a user tonavigate a course or environment or perform other visuo-motor activitymay require the individual to make movements (such as but not limited tosteering) that are detected and/or measured using at least one type ofthe sensor device. The data from the detection or measurement providesthe response to the data indicative of the response.

As a non-limiting example, an individual's response to a task orinterference rendered at the user interface that requires a user todiscriminate between a target and a non-target may require theindividual to make movements (such as but not limited to tapping orother spatially or temporally discriminating indication) that aredetected and/or measured using at least one type of the sensor device.The data that is collected by a component of the system or apparatusbased on the detection or other measurement of the individual'smovements (such as but not limited to at least one sensor or otherdevice or component described herein) provides the data indicative ofthe individual's responses.

The example system, method, and apparatus can be configured to apply theclassifier model, using computational techniques and machine learningtools, such as but not limited to linear/logistic regression, principalcomponent analysis, generalized linear mixed models, random decisionforests, support vector machines, or artificial neural networks, to thedata indicative of the individual's response to the tasks and/orinterference, and/or data from one or more physiological measures, tocreate composite variables or profiles that are more sensitive than eachmeasurement alone for generating a classifier output indicative of thecognitive response capabilities of the individual. In an example, theclassifier output can be configured for other indications such as butnot limited to detecting an indication of a disease, disorder orcognitive condition, or assessing cognitive health.

The example response classifiers herein can be trained to be applied todata collected from interaction sessions of individuals with thecognitive platform to provide the output. In a non-limiting example, theclassifier model can be used to generate a standards table, which can beapplied to the data collected from the individual's response to taskand/or interference to classify the individual's cognitive responsecapabilities.

Non-limiting examples of assessment of cognitive abilities includeassessment scales or surveys such as the Mini Mental State Exam, CANTABcognitive battery, Test of Variables of Attention (TOVA), RepeatableBattery for the Assessment of Neuropsychological Status, Clinical GlobalImpression scales relevant to specific conditions, Clinician'sInterview-Based Impression of Change, Severe Impairment Battery,Alzheimer's Disease Assessment Scale, Positive and Negative SyndromeScale, Schizophrenia Cognition Rating Scale, Conners Adult ADHD RatingScales, Hamilton Rating Scale for Depression, Hamilton Anxiety Scale,Montgomery-Asberg Depressing Rating scale, Young Mania Rating Scale,Children's Depression Rating Scale, Penn State Worry Questionnaire,Hospital Anxiety and Depression Scale, Aberrant Behavior Checklist,Activities for Daily Living scales, ADHD self-report scale, Positive andNegative Affect Schedule, Depression Anxiety Stress Scales, QuickInventory of Depressive Symptomatology, and PTSD Checklist.

In other examples, the assessment may test specific functions of a rangeof cognitions in cognitive or behavioral studies, including tests forperceptive abilities, reaction and other motor functions, visual acuity,long-term memory, working memory, short-term memory, logic, anddecision-making, and other specific example measurements, including butare not limited to TOVA, MOT (motion-object tracking), SART, CDT (Changedetection task), UFOV (useful Field of view), Filter task, WAIS digitsymbol, Troop, Simon task, Attentional Blink, N-back task, PRP task,task-switching test, and Flanker task.

In non-limiting examples, the example systems, methods, and apparatusaccording to the principles described herein can be applicable to manydifferent types of neuropsychological conditions, such as but notlimited to dementia, Parkinson's disease, cerebral amyloid angiopathy,familial amyloid neuropathy, Huntington's disease, or otherneurodegenerative condition, autism spectrum disorder (ASD), presence ofthe 16p11.2 duplication, and/or an executive function disorder, such asbut not limited to attention deficit hyperactivity disorder (ADHD),sensory-processing disorder (SPD), mild cognitive impairment (MCI),Alzheimer's disease, multiple-sclerosis, schizophrenia, major depressivedisorder (MDD), or anxiety.

The instant disclosure is directed to computer-implemented devicesformed as example cognitive platforms configured to implement softwareand/or other processor-executable instructions for the purpose ofmeasuring data indicative of a user's performance at one or more tasks,to provide a user performance metric. The example performance metric canbe used to derive an assessment of a user's cognitive abilities and/orto measure a user's response to a cognitive treatment, and/or to providedata or other quantitative indicia of a user's condition (includingphysiological condition and/or cognitive condition). Non-limitingexample cognitive platforms according to the principles herein can beconfigured to classify an individual as to a neuropsychologicalcondition, autism spectrum disorder (ASD), presence of the 16p11.2duplication, and/or an executive function disorder, and/or potentialefficacy of use of the cognitive platform when the individual is beingadministered (or about to be administered) a drug, biologic or otherpharmaceutical agent, based on the data collected from the individual'sinteraction with the cognitive platform and/or metrics computed based onthe analysis (and associated computations) of that data. Yet othernon-limiting example cognitive platforms according to the principlesherein can be configured to classify an individual as to likelihood ofonset and/or stage of progression of a neuropsychological condition,including as to a neurodegenerative condition, based on the datacollected from the individual's interaction with the cognitive platformand/or metrics computed based on the analysis (and associatedcomputations) of that data. The neurodegenerative condition can be, butis not limited to, Alzheimer's disease, dementia, Parkinson's disease,cerebral amyloid angiopathy, familial amyloid neuropathy, orHuntington's disease.

Any classification of an individual as to likelihood of onset and/orstage of progression of a neurodegenerative condition according to theprinciples herein can be transmitted as a signal to a medical device,healthcare computing system, or other device, and/or to a medicalpractitioner, a health practitioner, a physical therapist, a behavioraltherapist, a sports medicine practitioner, a pharmacist, or otherpractitioner, to allow formulation of a course of treatment for theindividual or to modify an existing course of treatment, including todetermine a change in dosage of a drug, biologic or other pharmaceuticalagent to the individual or to determine an optimal type or combinationof drug, biologic or other pharmaceutical agent to the individual.

In any example herein, the cognitive platform can be configured as anycombination of a medical device platform, a monitoring device platform,a screening device platform, or other device platform.

The instant disclosure is also directed to example systems that includecognitive platforms that are configured for coupling with one or morephysiological or monitoring component and/or cognitive testingcomponent. In some examples, the systems include cognitive platformsthat are integrated with the one or more other physiological ormonitoring component and/or cognitive testing component. In otherexamples, the systems include cognitive platforms that are separatelyhoused from and configured for communicating with the one or morephysiological or monitoring component and/or cognitive testingcomponent, to receive data indicative of measurements made using suchone or more components.

In an example system, method, and apparatus herein, the task or theinterference can include a response-deadline procedure having theresponse-deadline; where the at least one adaptive procedure modifiesthe response-deadline to modify a performance characteristics of theindividual to an impulsive response profile or a conservative responseprofile.

In an example system, method, and apparatus herein, the processing unitcan be programmed to control the user interface to modify a temporallength of the response window associated with the response-deadlineprocedure.

In an example system, method, and apparatus herein, the processing unitcan be configured to control the user interface to modify a time-varyingcharacteristics of an aspect of the task or the interference rendered tothe user interface. For example, modifying the time-varyingcharacteristics of an aspect of the task or the interference can includeadjusting a temporal length of the rendering of the task or interferenceat the user interface between two or more sessions of interactions ofthe individual. As another example, the time-varying characteristics isone or more of a speed of an object, a rate of change of a facialexpression, a direction of trajectory of an object, a change oforientation of an object, at least one color of an object, a type of anobject, or a size of an object.

In an example system, method, and apparatus herein, the change in typeof object is effected using morphing from a first type of object to asecond type of object or rendering a blendshape as a proportionatecombination of the first type of object and the second type of object.

In an example system, method, and apparatus herein, the processing unitcan be further programmed to compute as the classifier output parametersindicative of one or more of a bias sensitivity derived from the dataindicative of the first response and the second response, a non-decisiontime sensitivity to parallel tasks, a belief accumulation sensitivity toparallel task demands, a reward rate sensitivity, or a response windowestimation efficiency.

In an example system, method, and apparatus herein, the processing unitcan be further programmed to control the user interface to render thetask as a continuous visuo-motor tracking task.

In an example system, method, and apparatus herein, the processing unitcontrols the user interface to render the interference as a targetdiscrimination task.

As used herein, a target discrimination task may also be referred to asa perceptual reaction task, in which the individual is instructed toperform a two-feature reaction task including target stimuli andnon-target stimuli through a specified form of response. As anon-limiting example, that specified type of response can be for theindividual to make a specified physical action in response to a targetstimulus (e.g., move or change the orientation of a device, tap on asensor-coupled surface such as a screen, move relative to an opticalsensor, make a sound, or other physical action that activates a sensordevice) and refrain from making such specified physical action inresponse to a non-target stimulus.

In a non-limiting example, the individual is required to perform avisuomotor task (as a primary task) with a target discrimination task asan interference (secondary task). To effect the visuomotor task, aprogrammed processing unit renders visual stimuli that require finemotor movement as reaction of the individual to the stimuli. In someexamples, the visumotor task is a continuous visuomotor task. Theprocessing unit is programmed to alter the visual stimuli and recordingdata indicative of the motor movements of the individual over time(e.g., at regular intervals including 1, 5, 10, or 30 times per second).Example stimuli rendered using the programmed processing unit for avisuomotor task requiring fine motor movement may be a visualpresentation of a path that an avatar is required to remain within. Theprogrammed processing unit may render the path with certain types ofobstacles that the individual is either required to avoid or to navigatetowards. In an example, the fine motor movements effect by theindividual, such as but not limited to tilting or rotating a device, aremeasured using an accelerometer and/or a gyroscope (e.g., to steer orotherwise guide the avatar on the path while avoiding or crossing theobstacles as specified). The target discrimination task (serving as theinterference), can be based on targets and non-targets that differ inshape and/or color.

In some examples, the task and/or interference can be a visuomotor task,a target discrimination task, and/or a memory task.

Within the context of a computer-implemented adaptive response-deadlineprocedure, the response-deadline can be adjusted between trials orblocks of trials to manipulate the individual's performancecharacteristics towards certain goals. A common goal is driving theindividual's average response accuracy towards a certain value bycontrolling the response deadline.

Measurements at different response deadlines can provide different dataas to the shape and/or area of their decision boundary, so thecomputer-implemented adaptive procedure can inform the calculation ofthe impulsiveness strategy metric.

In a non-limiting example, the metric from signal detection theoryrepresenting cognitive function may be the hit rate from a targetdiscrimination task. In that context, hit rate may be defined as thenumber of correct responses to a target stimuli divided by the totalnumber of target stimuli presented, or the false alarm rate (e.g., thenumber of responses to a distractor stimuli divided by the number ofdistractor stimuli presented), the miss rate (e.g., the number ofnonresponses to a target stimuli divided by the number of incorrectresponses, including the nonresponses to a target stimuli added to thenumber of responses to a distractor stimuli), the correct response rate(the proportion of correct responses not containing signal). In anexample, the correct response rate may be calculated as the number ofnon-responses to the distractor stimuli divided by the number ofnon-responses to the distractor stimuli plus the number of responses tothe target stimuli.

An example system, method, and apparatus according to the principlesherein can be configured to apply adaptive performance procedures tomodify measures of performance to a specific stimulus intensity. Theprocedure can be adapted based on a percent correct (PC) or a D-Prime(d′) signal detection metric of sensitivity to a target. In an examplesystem, the value of percent correct (i.e., percent of correct responsesof the individual to a task) or D-prime may be used in the adaptivealgorithms as the basis for adapting the stimulus level of tasks and/orinterferences rendered at the user interface for user interaction fromone trial to another. However, the inventors have unexpectedly foundthat an adaptive procedure based on a computational model of humandecision-making (such as but not limited to the modified DDM),classifiers built from outputs of such models, and the analysisdescribed herein based on the output of the computational model, can bemore quantitatively informative on individual differences or on changesin sensitivity to a specific stimulus level. The decision boundarymetric (such as but not limited to the response criterion) provides aflexible tool for determining a tendency of an individual to provide aparticular type of response. Accordingly, an adaptation procedure basedon decision boundary metric (such as but not limited to the responsecriterion) measurements at the individual or group level become adesirable source of information about impulsive or conservative responsestrategies at the time of measurement and also as a quantifier of thechanges in performance at the individual or group level over time withrepeated measurements.

Executive function training, such as that delivered by the examplesystems, methods, and apparatus described herein can be configured toapply an adaptive algorithm to modify the stimulus levels betweentrials, to move a user's response strategy as indicated by theirmeasured criterion to a more conservative or impulsive strategy,depending on the needs or preference of the individual or based on theclinical population receiving the treatment.

The example systems, methods, and apparatus described herein can beconfigured to apply an adaptive algorithm that is adapted based on thecomputed decision boundary metric (such as but not limited to theresponse criterion) as described herein to modify the difficulty levelsof the tasks and/or interference rendered at the user interface for userinteraction from one trial to another.

FIG. 9 shows an example plot representing a stimulus that is adapted ona single property that has a range of possible intensities. FIG. 9 showsa projected two-dimensional (2D) representation of a three-dimensional(3D) joint distribution composed of a stimuli in which the observerattends to multiple features at a time. FIG. 9 shows one of severaltechniques to measure the criterion of multi-dimensional stimulus. Inthis example, a combined PC of 80% or d′ of 1.81 for multi-dimensionalstimuli is located on the point labeled 900. The band 902 represents thepossible d′ resulting from the range of possible hit and false-alarmrates in a system or apparatus that adapts the tasks and/or interferencebased on an adaptive performance procedure where performance is directedto PC=80% correct. In FIG. 9 , the center noise distribution is centeredat (0,0), which is a simplification to constrain the band 902 ofpossible d′ locations, but in practice the noise distribution center canbe located anywhere on the axes, as long as the distance between thenoise and signal distributions are connected by a vector the length ofthe d′ value. Multi-dimensional criterions can be estimated forindividuals or groups of individuals, and produce an estimate ofconservative or impulsive response strategies at the time of measurementor as a response to training using the computing device. Adapting thetasks and/or interference based on the output from the responseclassifiers herein can provide for greater flexibility than adaptationbased on the percent correct.

In an example, the task and/or interference can be modified based on aniterative estimation of metrics by tracking current estimates andselecting the features, trajectory, and response window of the targetingtask, and level/type of parallel task interference for the next trial inorder to maximize information the trial can provide.

In some examples, the task and/or interference are adaptive tasks. Thetask and/or interference can be adapted or modified in difficulty levelbased on the decision boundary metric (such as but not limited to theresponse criterion), as described hereinabove. Such difficulty adaptionmay be used to determine the ability of the participant.

In an example, the difficulty of the task adapts with every stimuli thatis presented, which could occur more often than once at regular timeintervals (e.g., every 5 seconds, every 10 seconds, every 20 seconds orother regular schedule).

In another example, the difficulty of a continuous task can be adaptedon a set schedule, such as but not limited to every 30 seconds, 10seconds, 1 second, 2 times per second, or 30 times per second.

In an example, the length of time of a trial depends on the number ofiterations of rendering (of the tasks/interference) and receiving (ofthe individual's responses) and can vary in time. In an example, a trialcan be on the order of about 500 milliseconds, about 1 second (s), about10 s, about 20 s, about 25 s, about 30 s, about 45 s, about 60 s, about2 minutes, about 3 minutes, about 4 minutes, about 5 minutes, orgreater. Each trial may have a pre-set length or may be dynamically setby the processing unit (e.g., dependent on an individual's performancelevel or a requirement of the adapting from one level to another).

In an example, the task and/or interference can be modified based ontargeting changes in one or more specific metrics by selecting features,trajectory, and response window of the targeting task, and level/type ofparallel task interference to progressively require improvements inthose metrics in order for the apparatus to indicate to an individualthat they have successfully performed the task. This could includespecific reinforcement, including explicit messaging, to guide theindividual to modify performance according to the desired goals.

In an example, the task and/or interference can be modified based on acomparison of an individual's performance with normative data or acomputer model or taking user input (the individual performing thetask/interference or another individual such as a clinician) to select aset of metrics to target for changing in a specific order, anditeratively modifying this procedure based on the subject's response totreatment. This could include feedback to the individual performing thetask/interference or another individual to serve as notification ofchanges to the procedure, potentially enabling them to approve or modifythese changes before they take effect.

In various examples, the difficulty level may be kept constant or may bevaried over at least a portion of a session in an adaptiveimplementation, where the adaptive task (primary task or secondary task)increases or decreases in difficulty based on the decision boundarymetric (such as but not limited to the response criterion).

An example system, method, and apparatus according to the principlesherein can be configured to enhance the cognitive skills in anindividual. In an example implementation, a programmed processing unitis configured to execute processor-executable instructions to render atask with an interference at a user interface. As described in greaterdetail herein, one or more of the task and the interference can betime-varying and have a response deadline, such that the user interfaceimposes a limited time period for receiving at least one type ofresponse from the individual interacting with the apparatus or system.The processing unit is configured to control the user interface tomeasure data indicative of two or more differing types of responses tothe task or to the interference. The programmed processing unit isfurther configured to execute processor-executable instructions to causethe example system or apparatus to receive data indicative of a firstresponse of the individual to the task and a second response of theindividual to the interference, analyze at least some portion of thedata to compute at least one response profile representative of theperformance of the individual, and determine a decision boundary metric(such as but not limited to the response criterion) from the responseprofile. As described herein, including in connection with FIGS. 4A and4B, the decision boundary metric (such as but not limited to theresponse criterion) gives a quantitative measure of a tendency of theindividual to provide at least one type of response of the two or morediffering types of responses (Response A vs. Response B) to the task orthe interference. The programmed processing unit is further configuredto execute processor-executable instructions to adapt the task and/orthe interference to derive a modification in the computed decisionboundary metric (such as but not limited to the response criterion) suchthat the first response and/or the second response is modified, therebyindicating a modification of the cognitive response capabilities of theindividual.

In an example, the indication of the modification of the cognitiveresponse capabilities can be based on observation of a change in ameasure of a degree of impulsiveness or conservativeness of theindividual's cognitive response capabilities.

In an example, the indication of the modification of the cognitiveresponse capabilities can include a change in a measure of one or moreof sustained attention, selective attention, attention deficit,impulsivity, inhibition, perceptive abilities, reaction and other motorfunctions, visual acuity, long-term memory, working memory, short-termmemory, logic, and decision-making.

In an example, adapting the task and/or interference based on the firstdecision boundary metric (such as but not limited to the responsecriterion) includes one or more of modifying the temporal length of theresponse window, modifying a type of reward or rate of presentation ofrewards to the individual, and modifying a time-varying characteristicof the task and/or interference.

In an example, modifying the time-varying characteristics of an aspectof the task or the interference can include adjusting a temporal lengthof the rendering of the task or interference at the user interfacebetween two or more sessions of interactions of the individual.

In an example, the time-varying characteristics can include one or moreof a speed of an object, a rate of change of a facial expression, adirection of trajectory of an object, a change of orientation of anobject, at least one color of an object, a type of an object, or a sizeof an object, or modifying a sequence or balance of rendering of targetsversus non-targets at the user interface.

In an example, the change in type of object is effected using morphingfrom a first type of object to a second type of object or rendering ablendshape as a proportionate combination of the first type of objectand the second type of object.

Designing the computer-implemented adaptive procedure using a goal ofexplicitly measuring the shape and/or area of the decision boundary, theresponse deadlines can be adjusted to points where measurements producemaximal information of use for defining this boundary. These optimaldeadlines may be determined using an information theoretic approach tominimize the expected information entropy.

Example systems, methods and apparatus according to the principlesherein can be implemented using a programmed computing device includingat least one processing unit, to determine a potential biomarker forclinical populations.

Example systems, methods and apparatus according to the principlesherein can be implemented using a programmed computing device includingat least one processing unit as a metric for individuals and groups toassess tendency for impulsive and/or conservative response strategies.

Example systems, methods and apparatus according to the principlesherein can be implemented using a programmed computing device includingat least one processing unit to improve computer-implemented adaptiveprocedures to compensate for impulsive or conservative responseprofiles.

Example systems, methods and apparatus according to the principlesherein can be implemented using a programmed computing device includingat least one processing unit to measure change in response profile inindividuals or groups after use of an intervention.

Example systems, methods and apparatus according to the principlesherein can be implemented using a programmed computing device includingat least one processing unit to apply the example metrics herein, to addanother measurable characteristic of individual or group data that canbe implemented for greater measurement of psychophysical-thresholdaccuracy and assessment of response profile to computer-implementedadaptive psychophysical procedures.

Example systems, methods and apparatus according to the principlesherein can be implemented using a programmed computing device includingat least one processing unit to apply the example metrics herein to adda new dimension to available data that can be used to increase theamount of information harvested from psychophysical testing.

An example system, method, and apparatus according to the principlesherein can be configured to enhance the cognitive skills in anindividual. In an example implementation, a programmed processing unitis configured to execute processor-executable instructions to render atask with an interference at a user interface. As described in greaterdetail herein, one or more of the task and the interference can betime-varying and have a response deadline, such that the user interfaceimposes a limited time period for receiving at least one type ofresponse from the individual interacting with the apparatus or system.The processing unit is configured to control the user interface tomeasure data indicative of two or more differing types of responses tothe task or to the interference. The programmed processing unit isfurther configured to execute processor-executable instructions to causethe example system or apparatus to receive data indicative of a firstresponse of the individual to the task and a second response of theindividual to the interference (from a first session), analyze at leastsome portion of the data to compute a first response profilerepresentative of the first performance of the individual, and determinea first decision boundary metric (such as but not limited to theresponse criterion) from the response profile. As described herein,including in connection with FIGS. 4A and 4B, the decision boundarymetric (such as but not limited to the response criterion) gives aquantitative measure of a tendency of the individual to provide at leastone type of response of the two or more differing types of responses(Response A vs. Response B) to the task or the interference. Theprogrammed processing unit is further configured to executeprocessor-executable instructions to adapt the task and/or theinterference based on the computed first decision boundary metric (suchas but not limited to the response criterion) (to generate a secondsession), receive data indicative of the first response of theindividual to the task and the second response of the individual to theinterference, analyze at least some portion of the data to compute asecond response profile and a second decision boundary metric (such asbut not limited to the response criterion) representative of the secondperformance of the individual. The programmed processing unit is furtherconfigured to execute processor-executable instructions, based on thefirst decision boundary metric (such as but not limited to the responsecriterion) and second decision boundary metric (such as but not limitedto the response criterion), to generate an output to the user interfaceindicative of one or more of: (i) a likelihood of the individualexperiencing an adverse event in response to administration of thepharmaceutical agent, drug, or biologic, (ii) a change in one or more ofthe amount, concentration, or dose titration of a pharmaceutical agent,drug, biologic or other medication being or to be administered to anindividual, and (iii) a change in the individual's cognitive responsecapabilities, a recommended treatment regimen, or recommending ordetermining a degree of effectiveness of at least one of a behavioraltherapy, counseling, or physical exercise.

In a non-limiting example, based on the results of the analysis of thefirst decision boundary metric (such as but not limited to the responsecriterion) and the second decision boundary metric (such as but notlimited to the response criterion), a medical, healthcare, or otherprofessional (with consent of the individual) can gain a betterunderstanding of an individual's cognitive response capabilities, andpotentially more specifically identify the type of cognitive condition,executive function disorder, or disease that could be affecting anindividual's cognitive abilities (including by reviewing the results ofthe analysis in conjunction with other physiological, behavioral, and/ordiagnostic measures). For example, the results may be used to identifyindividuals of a group who may better benefit from a first type ofpharmaceutical agent, drug, biologic, or other medication, while otherindividuals in the group could benefit from a second type.

In a non-limiting example, based on the results of the analysis of thefirst decision boundary metric (such as but not limited to the responsecriterion) and the second decision boundary metric (such as but notlimited to the response criterion), a medical, healthcare, or otherprofessional (with consent of the individual) can gain a betterunderstanding of potential adverse events which may occur (orpotentially are occurring) if the individual is administered aparticular type of, amount, concentration, or dose titration of apharmaceutical agent, drug, biologic, or other medication, includingpotentially affecting cognition.

In a non-limiting example, a searchable database is provided herein thatincludes data indicative of the results of the analysis of the firstdecision boundary metric (such as but not limited to the responsecriterion) and the second decision boundary metric (such as but notlimited to the response criterion) for particular individuals, alongwith known levels of efficacy of at least one types of pharmaceuticalagent, drug, biologic, or other medication experiences by theindividuals, and/or quantifiable information on one or more adverseevents experienced by the individual with administration of the at leastone types of pharmaceutical agent, drug, biologic, or other medication.The searchable database can be configured to provide metrics for use todetermine whether a given individual is a candidate for benefiting froma particular type of pharmaceutical agent, drug, biologic, or othermedication based on the response measures, response profiles, and/ordecision boundary metric (such as but not limited to response criteria)obtained for the individual in interacting with the task and/orinterference rendered at the computing device.

As a non-limiting example, based on data indicative of a userinteraction with the tasks and/or interference rendered at a userinterface of a computing device, the decision boundary metric (such asbut not limited to the response criterion) could provide information onthe tendency of an individual to a particular type of response, such asbut not limited to the degree of impulsiveness or conservativeness ofthe individual's cognitive response strategy. This data can assist withidentifying a treatment regimen, or a degree of effectiveness of abehavioral therapy, counseling, and/or physical exercise.

In a non-limiting example, based on data indicative of a userinteraction with the tasks and/or interference rendered at a userinterface of a computing device, the decision boundary metric (such asbut not limited to the response criterion) could provide information onthe individual, based on the degree of impulsiveness or conservativenessof the individual's cognitive response strategy. This data can assistwith identifying whether the individual is a candidate for a particulartype of drug (such as but not limited to a stimulant, e.g.,methylphenidate or amphetamine) or whether it might be beneficial forthe individual to have the drug administered in conjunction with aregiment of specified repeated interactions with the tasks and/orinterference rendered to the computing device. Other non-limitingexamples of a biologic, drug or other pharmaceutical agent applicable toany example described herein include methylphenidate (MPH), scopolamine,donepezil hydrochloride, rivastigmine tartrate, memantine HCl,solanezumab, aducanumab, and crenezumab.

Another example system, method, and apparatus according to theprinciples herein can be configured to enhance the cognitive skills inan individual. In an example implementation, a programmed processingunit is configured to execute processor-executable instructions torender a task with an interference at a user interface. As described ingreater detail herein, one or more of the task and the interference canbe time-varying and have a response deadline, such that the userinterface imposes a limited time period for receiving at least one typeof response from the individual interacting with the apparatus orsystem. The processing unit is configured to control the user interfaceto receive data indicative of one or more of an amount, concentration,or dose titration of a pharmaceutical agent, drug, or biologic being orto be administered to an individual, and to measure data indicative oftwo or more differing types of responses to the task or to theinterference. The programmed processing unit is further configured toexecute processor-executable instructions to cause the example system orapparatus to receive data indicative of a first response of theindividual to the task and a second response of the individual to theinterference (from a first session), analyze at least some portion ofthe data to compute a first response profile representative of the firstperformance of the individual, and determine a first decision boundarymetric (such as but not limited to the response criterion) from theresponse profile. As described herein, including in connection withFIGS. 4A and 4B, the decision boundary metric (such as but not limitedto the response criterion) gives a quantitative measure of a tendency ofthe individual to provide at least one type of response of the two ormore differing types of responses (Response A vs. Response B) to thetask or the interference. The programmed processing unit is furtherconfigured to execute processor-executable instructions to adapt thetask and/or the interference based on the first decision boundary metric(such as but not limited to the response criterion) and the amount orconcentration of a pharmaceutical agent, drug, or biologic (to generatea second session), receive data indicative of the first response of theindividual to the task and the second response of the individual to theinterference, analyze at least some portion of the data to compute asecond response profile and a second decision boundary metric (such asbut not limited to the response criterion) representative of the secondperformance of the individual. The programmed processing unit is furtherconfigured to execute processor-executable instructions, based on thefirst decision boundary metric (such as but not limited to the responsecriterion) and second decision boundary metric (such as but not limitedto the response criterion), to generate an output to the user interfaceindicative of one or more of: (i) a likelihood of the individualexperiencing an adverse event in response to administration of thepharmaceutical agent, drug, or biologic, (ii) a recommended change inone or more of the amount, concentration, or dose titration of thepharmaceutical agent, drug, biologic or other medication, and (iii) achange in the individual's cognitive response capabilities, arecommended treatment regimen, or recommending or determining a degreeof effectiveness of at least one of a behavioral therapy, counseling, orphysical exercise.

In a non-limiting example, based on the results of the analysis of thefirst decision boundary metric (such as but not limited to the responsecriterion) and the second decision boundary metric (such as but notlimited to the response criterion), a medical, healthcare, or otherprofessional (with consent of the individual) can gain a betterunderstanding of potential adverse events which may occur (orpotentially are occurring) if the individual is administered a differentamount, concentration, or dose titration of a pharmaceutical agent,drug, biologic, or other medication, including potentially affectingcognition.

In a non-limiting example, a searchable database is provided herein thatincludes data indicative of the results of the analysis of the firstdecision boundary metric (such as but not limited to the responsecriterion) and the second decision boundary metric (such as but notlimited to the response criterion) for particular individuals, alongwith known levels of efficacy of at least one types of pharmaceuticalagent, drug, biologic, or other medication experiences by theindividuals, and/or quantifiable information on one or more adverseevents experienced by the individual with administration of the at leastone types of pharmaceutical agent, drug, biologic, or other medication.The searchable database can be configured to provide metrics for use todetermine whether a given individual is a candidate for benefiting froma particular type of pharmaceutical agent, drug, biologic, or othermedication based on the response measures, response profiles, and/ordecision boundary metric (such as but not limited to response criteria)obtained for the individual in interacting with the task and/orinterference rendered at the computing device. As a non-limitingexample, based on data indicative of a user interaction with the tasksand/or interference rendered at a user interface of a computing device,the decision boundary metric (such as but not limited to the responsecriterion) could provide information on the individual, based on thedegree of impulsiveness or conservativeness of the individual'scognitive response strategy. This data can assist with identifyingwhether the individual is a candidate for a particular type of drug(such as but not limited to a stimulant, e.g., methylphenidate oramphetamine) or whether it might be beneficial for the individual tohave the drug administered in conjunction with a regiment of specifiedrepeated interactions with the tasks and/or interference rendered to thecomputing device. Other non-limiting examples of a biologic, drug orother pharmaceutical agent applicable to any example described hereininclude methylphenidate (MPH), scopolamine, donepezil hydrochloride,rivastigmine tartrate, memantine HCl, solanezumab, aducanumab, andcrenezumab.

In an example, the change in the individual's cognitive responsecapabilities comprises an indication of a change in degree ofimpulsiveness or conservativeness of the individual's cognitive responsestrategy.

As a non-limiting example, given that impulsive behavior is attendantwith ADHD, an example cognitive platform that is configured fordelivering treatment (including of executive function) may promote lessimpulsive behavior in a regimen. This may target dopamine systems in thebrain, increasing normal regulation, which may result in a transfer ofbenefits of the reduction of impulsive behavior to the everyday life ofan individual.

Stimulants such as methylphenidate and amphetamine are also administeredto individuals with ADHD, to increase levels of norepinephrine anddopamine in the brain. Their cognitive effects may be attributed totheir actions at the prefrontal cortex, however, there may not beremediation of cognitive control deficits or other cognitive abilities.An example cognitive platform herein can be configured for deliveringtreatment (including of executive function) to remediate an individual'scognitive control deficit.

The use of the example systems, methods, and apparatus according to theprinciples described herein can be applicable to many different types ofneuropsychological conditions, such as but not limited to dementia,Parkinson's disease, cerebral amyloid angiopathy, familial amyloidneuropathy, Huntington's disease, or other neurodegenerative condition,autism spectrum disorder (ASD), presence of the 16p11.2 duplication,and/or an executive function disorder, such as but not limited toattention deficit hyperactivity disorder (ADHD), sensory-processingdisorder (SPD), mild cognitive impairment (MCI), Alzheimer's disease,multiple-sclerosis, schizophrenia, major depressive disorder (MDD), oranxiety.

In any example implementation, data and other information from anindividual is collected, transmitted, and analyzed with their consent.

As a non-limiting example, the cognitive platform described inconnection with any example system, method and apparatus herein,including a cognitive platform based on interference processing, can bebased on or include the Project: EVO™ platform by Akili InteractiveLabs, Inc., Boston, MA.

Non-Limiting Example Tasks and Interference

The effects of interference processing on the cognitive controlabilities of individuals has been reported. See, e.g., A. Anguera,Nature vol. 501, p. 97 (Sep. 5, 2013) (the “Nature article”). See, also,U.S. Publication No. 20140370479A1 (U.S. application Ser. No.13/879,589), filed on Nov. 10, 2011, which is incorporated herein byreference. Some of those cognitive abilities include cognitive controlabilities in the areas of attention (selectivity, sustainability, etc.),working memory (capacity and the quality of information maintenance inworking memory) and goal management (ability to effectively parallelprocess two attention-demanding tasks or to switch tasks). As anexample, children diagnosed with ADHD (attention deficit hyperactivitydisorder) exhibit difficulties in sustaining attention. Attentionselectivity was found to depend on neural processes involved in ignoringgoal-irrelevant information and on processes that facilitate the focuson goal-relevant information. The publications report neural datashowing that when two objects are simultaneously placed in view,focusing attention on one can pull visual processing resources away fromthe other. Studies were also reported showing that memory depended moreon effectively ignoring distractions, and the ability to maintaininformation in mind is vulnerable to interference by both distractionand interruption. Interference by distraction can be, e.g., aninterference that is a non-target, that distracts the individual'sattention from the primary task, but that the instructions indicate theindividual is not to respond to. Interference byinterruption/interruptor can be, e.g., an interference that is a targetor two or more targets, that also distracts the individual's attentionfrom the primary task, but that the instructions indicate the individualis to respond to (e.g., for a single target) or choose between/among(e.g., a forced-choose situation where the individual decides betweendiffering degrees of a feature).

There were also fMRI results reported showing that diminished memoryrecall in the presence of a distraction can be associated with adisruption of a neural network involving the prefrontal cortex, thevisual cortex, and the hippocampus (involved in memory consolidation).Prefrontal cortex networks (which play a role in selective attention)can be vulnerable to disruption by distraction. The publications alsoreport that goal management, which requires cognitive control in theareas of working memory or selective attention, can be impacted by asecondary goal that also demands cognitive control. The publicationsalso reported data indicating beneficial effects of interferenceprocessing as an intervention with effects on an individual's cognitiveabilities, including to diminish the detrimental effects of distractionsand interruptions. The publications described cost measures that can becomputed (including an interference cost) to quantify the individual'sperformance, including to assess single-tasking or multitaskingperformance.

An example cost measure disclosed in the publications is the percentagechange in an individual's performance at a single-tasking task ascompared to a multi-tasking task, such that greater cost (that is, amore negative percentage cost) indicates increased interference when anindividual is engaged in single-tasking vs multi-tasking.

The tangible benefits of computer-implemented interference processingare also reported. For example, the Nature paper states thatmulti-tasking performance assessed using computer-implementedinterference processing was able to quantify a linear age-relateddecline in performance in adults from 20 to 79 years of age. The Naturepaper also reports that older adults (60 to 85 years old) who interactedwith an adaptive form of the computer-implemented interferenceprocessing exhibited reduced multitasking costs, with the gainspersisting for six (6) months. The Nature paper also reported thatage-related deficits in neural signatures of cognitive control, asmeasured with electroencephalography, were remediated by themultitasking training (using the computer-implemented interferenceprocessing), with enhanced midline frontal theta power andfrontal-posterior theta coherence. Interacting with thecomputer-implemented interference processing resulted in performancebenefits that extended to untrained cognitive control abilities(enhanced sustained attention and working memory), with an increase inmidline frontal theta power predicting a boost in sustained attentionand preservation of multitasking improvement six (6) months later.

The example systems, methods, and apparatus according to the principlesherein are configured to classify an individual as to cognitiveabilities and/or to enhance those cognitive abilities based onimplementation of interference processing using a computerized cognitiveplatform. The example systems, methods, and apparatus are configured toimplement a form of multi-tasking using the capabilities of a programmedcomputing device, where an individual is required to perform a task andan interference substantially simultaneously, and the sensing andmeasurement capabilities of the computing device are configured tocollect data indicative of the physical actions taken by the individualduring the response execution time to respond to the task atsubstantially the same time as the computing device collects the dataindicative of the physical actions taken by the individual to respond tothe interference. The capabilities of the computing devices andprogrammed processing units to render the task and/or the interferencein real time to a user interface, and to measure the data indicative ofthe individual's responses to the task and/or the interference in realtime and substantially simultaneously can provide quantifiable measuresof an individual's cognitive capabilities to rapidly switch to and fromdifferent tasks and interferences or to perform multiple, different,tasks or interferences in a row (including for single-tasking, where theindividual is required to perform a single type of task for a set periodof time).

In any example herein, the task and/or interference includes a responsedeadline, such that the user interface imposes a limited time period forreceiving at least one type of response from the individual interactingwith the apparatus or computing device. For example, the period of timethat an individual is required to interact with a computing device orother apparatus to perform a task and/or an interference can be apredetermined amount of time, such as but not limited to about 30seconds, about 1 minute, about 4 minutes, about 7 minutes, about 10minutes, or greater than 10 minutes.

The example systems, methods, and apparatus can be configured toimplement a form of multi-tasking to provide measures of theindividual's capabilities in deciding whether to perform one actioninstead of another and to activate the rules of the current task in thepresence of an interference such that the interference diverts theindividual's attention from the task, as a measure of an individual'scognitive abilities in executive function control.

The example systems, methods, and apparatus can be configured toimplement a form of single-tasking, where measures of the individual'sperformance at interacting with a single type of task (i.e., with nointerference) for a set period of time (such as but not limited tonavigation task only or a target discriminating task only) can also beused to provide measure of an individual's cognitive abilities.

The example systems, methods, and apparatus can be configured toimplement sessions that involve differing sequences and combinations ofsingle-tasking and multi-tasking trials. In a first exampleimplementation, a session can include a first single-tasking trial (witha first type of task), a second single-tasking trial (with a second typeof task), and a multi-tasking trial (a primary task rendered with aninterference). In a second example implementation, a session can includetwo or more multi-tasking trials (a primary task rendered with aninterference). In a third example implementation, a session can includetwo or more single-tasking trials (all based on the same type of tasksor at least one being based on a different type of task).

The performance can be further analyzed to compare the effects of twodifferent types of interference (e.g. distraction or interruptor) on theperformances of the various tasks. Some comparisons can includeperformance without interference, performance with distraction, andperformance with interruption. The cost of each type of interference(e.g. distraction cost and interruptor/multi-tasking cost) on theperformance level of a task is analyzed and reported to the individual.

In any example herein, the interference can a secondary task thatincludes a stimulus that is either a non-target (as a distraction) or atarget (as an interruptor), or a stimulus that is differing types oftargets (e.g., differing degrees of a facial expression or othercharacteristic/feature difference).

Based on the capability of a programmed processing unit to control theeffecting of multiple separate sources (including sensors and othermeasurement components) and the receiving of data selectively from thesemultiple different sources at substantially simultaneously (i.e., atroughly the same time or within a short time interval) and in real-time,the example systems, methods, and apparatus herein can be used tocollect quantitative measures of the responses form an individual to thetask and/or interference which could not be achieved using normal humancapabilities. As a result, the example systems, methods, and apparatusherein can be configured to implement a programmed processing unit torender the interference substantially simultaneously with the task overcertain time periods.

In some example implementations, the example systems, methods, andapparatus herein also can be configured to receive the data indicativeof the measure of the degree and type of the individual's response tothe task substantially simultaneously as the data indicative of themeasure of the degree and type of the individual's response to theinterference is collected (whether the interference includes a target ora non-target). In some examples, the example systems, methods, andapparatus are configured to perform the analysis by applying scoring orweighting factors to the measured data indicative of the individual'sresponse to a non-target that differ from the scoring or weightingfactors applied to the measured data indicative of the individual'sresponse to a target, in order to compute an interference cost.

In some example implementations, the example systems, methods, andapparatus herein also can be configured to selectively receive dataindicative of the measure of the degree and type of the individual'sresponse to an interference that includes a target stimulus (i.e., aninterruptor) substantially simultaneously (i.e., at substantially thesame time) as the data indicative of the measure of the degree and typeof the individual's response to the task is collected and to selectivelynot collect the measure of the degree and type of the individual'sresponse to an interference that includes a non-target stimulus (i.e., adistraction) substantially simultaneously (i.e., at substantially thesame time) as the data indicative of the measure of the degree and typeof the individual's response to the task is collected. That is, theexample systems, methods, and apparatus are configured to discriminatebetween the windows of response of the individual to the target versusnon-target by selectively controlling the state of thesensing/measurement components for measuring the response eithertemporally and/or spatially. This can be achieved by selectivelyactivating or de-activating sensing/measurement components based on thepresentation of a target or non-target, or by receiving the datameasured for the individual's response to a target and selectively notreceiving (e.g., disregarding, denying, or rejecting) the data measuredfor the individual's response to a non-target.

As described herein, using the example systems, methods, and apparatusherein can be implemented to provide a measure of the cognitiveabilities of an individual in the area of attention, including based oncapabilities for sustainability of attention over time, selectivity ofattention, and reduction of attention deficit. Other areas of anindividual's cognitive abilities that can be measured using the examplesystems, methods, and apparatus herein include impulsivity, inhibition,perceptive abilities, reaction and other motor functions, visual acuity,long-term memory, working memory, short-term memory, logic, anddecision-making.

As described herein, using the example systems, methods, and apparatusherein can be implemented to adapt the tasks and/or is the most criticaldesign element for any effective plasticity-harnessing tool. Also, wewanted to control every single game element-timing, positioning, andnature of stimuli—so that we could record neural activity during gameplay and understand what was changing in the brain in response totraining.

FIGS. 10A-15V show non-limiting example user interfaces that can berendered using example systems, methods, and apparatus herein to renderthe tasks and/or interferences for user interactions. The non-limitingexample user interfaces of FIGS. 10A-15V also can be used for one ormore of: to display instructions to the individual for performing thetasks and/or interferences, to collect the data indicative of theindividual's responses to the tasks and/or the interferences, to showprogress metrics, and to provide the analysis metrics.

FIGS. 10A-10D show non-limiting example user interfaces rendered usingexample systems, methods, and apparatus herein. As shown in FIGS.10A-10B, an example programmed processing unit can be used to render tothe user interfaces (including graphical user interfaces) displayfeatures 1000 for displaying instructions to the individual forperforming the tasks and/or interferences, and metric features 1002 toshow status indicators from progress metrics and/or results fromapplication of analytics to the data collected from the individual'sinteractions (including the responses to tasks/interferences) to providethe analysis metrics. In any example systems, methods, and apparatusherein, the response classifier can be used to provide the analysismetrics provided as a response output. In any example systems, methods,and apparatus herein, the data collected from the user interactions canbe used as input to train the response classifier. As shown in FIGS.10A-10B, an example programmed processing unit also may be used torender to the user interfaces (including graphical user interfaces) anavatar or other processor-rendered guide 1004 that an individual isrequired to control (such as but not limited to navigate a path or otherenvironment in a visuo-motor task, and/or to select an object in atarget discrimination task). As shown in FIG. 10B, the display features1000 can be used to instruct the individual what is expected to performa navigation task while the user interface depicts (using the dashedline) the type of movement of the avatar or other processor-renderedguide 1004 required for performing the navigation task. As shown in FIG.10C, the display features 1000 can be used to instruct the individualwhat is expected to perform a target discrimination task while the userinterface depicts the type of object(s) 1006 and 1008 that may berendered to the user interface, with one type of object 1006 designatedas a target while the other type of object 1008 that may be rendered tothe user interface is designated as a non-target (e.g., by being crossedout in this example). As shown in FIG. 10D, the display features 1000can be used to instruct the individual what is expected to perform botha navigation task as a primary task and a target discrimination as asecondary task (i.e., an interference) while the user interface depicts(using the dashed line) the type of movement of the avatar or otherprocessor-rendered guide 1004 required for performing the navigationtask, and the user interface renders the object type designated as atarget object 1006 and the object type designated as a non-target object1008.

FIGS. 11A-11D show examples of the features of object(s) (targets ornon-targets) that can be rendered as time-varying characteristics to anexample user interface, according to the principles herein. FIG. 11Ashows an example where the modification to the time-varyingcharacteristics of an aspect of the object 1100 rendered to the userinterface is a dynamic change in position and/or speed of the object1100 relative to environment rendered in the graphical user interface.FIG. 11B shows an example where the modification to the time-varyingcharacteristics of an aspect of the object 1102 rendered to the userinterface is a dynamic change in size and/or direction oftrajectory/motion, and/or orientation of the object 1102 relative to theenvironment rendered in the graphical user interface. FIG. 11C shows anexample where the modification to the time-varying characteristics of anaspect of the object 1104 rendered to the user interface is a dynamicchange in shape or other type of the object 1104 relative to theenvironment rendered in the graphical user interface. In thisnon-limiting example, the time-varying characteristic of object 1104 iseffected using morphing from a first type of object (a star object) to asecond type of object (a round object). In another non-limiting example,the time-varying characteristic of object 1104 is effected by renderinga blendshape as a proportionate combination of a first type of objectand a second type of object. FIG. 11C shows an example where themodification to the time-varying characteristics of an aspect of theobject 1104 rendered to the user interface is a dynamic change in shapeor other type of the object 1104 rendered in the graphical userinterface (in this non-limiting example, from a star object to a roundobject). FIG. 11D shows an example where the modification to thetime-varying characteristics of an aspect of the object 1106 rendered tothe user interface is a dynamic change in pattern, or color, or visualfeature of the object 1106 relative to environment rendered in thegraphical user interface (in this non-limiting example, from a starobject having a first pattern to a round object having a secondpattern). In another non-limiting example, the time-varyingcharacteristic of object can be a rate of change of a facial expressiondepicted on or relative to the object.

FIGS. 12A-12T show a non-limiting example of the dynamics of tasks andinterferences that can be rendered at user interfaces, according to theprinciples herein. In this example, the task is a visuo-motor navigationtask, and the interference is target discrimination (as a secondarytask). As shown in FIGS. 12D, 12I-12K, and 12O-12Q, the individual isrequired to perform the navigation task by controlling the motion of theavatar 1202 along a path that coincides with the milestone objects 1204.FIGS. 12A-12T show a non-limiting example implementation where theindividual is expected to actuate an apparatus or computing device (orother sensing device) to cause the avatar 1202 to coincide with themilestone object 1204 as the response in the navigation task, withscoring based on the success of the individual at crossing paths with(e.g., hitting) the milestone objects 1204. In another example, theindividual is expected to actuate an apparatus or computing device (orother sensing device) to cause the avatar 1202 to miss the milestoneobject 1204, with scoring based on the success of the individual atavoiding the milestone objects 1204. FIGS. 12A-12C show the dynamics ofa target object 1206 (a star having a first type of pattern), where thetime-varying characteristic is the trajectory of motion of the object.FIGS. 12E-12H show the dynamics of a non-target object 1208 (a starhaving a second type of pattern), where the time-varying characteristicis the trajectory of motion of the object. FIGS. 12I-12T show thedynamics of other portions of the navigation task, where the individualis expected to guide the avatar 1202 to cross paths with the milestoneobject 1204 in the absence of an interference (a secondary task).

In the example of FIGS. 12A-12T, the processing unit of the examplesystem, method, and apparatus is configured to receive data indicativeof the individual's physical actions to cause the avatar 1202 tonavigate the path. For example, the individual may be required toperform physical actions to “steer” the avatar, e.g., by changing therotational orientation or otherwise moving a computing device. Suchaction can cause a gyroscope or accelerometer or other motion orposition sensor device to detect the movement, thereby providingmeasurement data indicative of the individual's degree of success inperforming the navigation task.

In the example of FIGS. 12A-12C and 12E-12H, the processing unit of theexample system, method, and apparatus is configured to receive dataindicative of the individual's physical actions to perform the targetdiscrimination task. For example, the individual may be instructed priorto a trial or other session to tap, or make other physical indication,in response to display of a target object 1206, and not to tap to makethe physical indication in response to display of a non-target object1208. In FIGS. 12A-12C and 12E-12H, the target discrimination task actsas an interference (i.e., a secondary task) to the primary navigationtask, in an interference processing multi-tasking implementation. Asdescribed hereinabove, the example systems, methods, and apparatus cancause the processing unit to render a display feature (e.g., displayfeature 1000) to display the instructions to the individual as to theexpected performance. As also described hereinabove, the processing unitof the example system, method, and apparatus can be configured to (i)receive the data indicative of the measure of the degree and type of theindividual's response to the primary task substantially simultaneouslyas the data indicative of the measure of the degree and type of theindividual's response to the interference is collected (whether theinterference includes a target or a non-target), or (i) to selectivelyreceive data indicative of the measure of the degree and type of theindividual's response to an interference that includes a target stimulus(i.e., an interruptor) substantially simultaneously (i.e., atsubstantially the same time) as the data indicative of the measure ofthe degree and type of the individual's response to the task iscollected and to selectively not collect the measure of the degree andtype of the individual's response to an interference that includes anon-target stimulus (i.e., a distraction) substantially simultaneously(i.e., at substantially the same time) as the data indicative of themeasure of the degree and type of the individual's response to the taskis collected

FIGS. 13A-13D show another non-limiting example of the dynamics of tasksand interferences that can be rendered at user interfaces, according tothe principles herein. In this example, the task is a visuo-motornavigation task, and the interference is target discrimination (as asecondary task), where an individual is required to perform physicalactions to cause an avatar 1302 to navigate to cross paths with themilestone object 1304 as the primary task and interact with an object1306 as target discrimination (interference as a secondary task). FIGS.13A-13D show an example of the type of reward 1308 that can be shown onthe graphical user interface responsive to the individual's indicationof selecting a target object. In this non-limiting example, the reward1308 is a set of rings that are rendered near the target 1306 atsubstantially the time the individual makes the second responseselecting the target. In a non-limiting example, the second response ismade by a tap, or other physical action to a portion of the userinterface based on the individual's decision to enter a response.

FIGS. 14A-14D show examples of the dynamics of an instructions panelrendered to a user interface of an example user interface. In thisexample, the processing unit causes the user interface to show thedynamics of movement of the instructions panel 1402 into view from theright side of the user interface. FIGS. 14A-14D also show non-limitingexamples of target objects 1404 and non-target objects 1406. In thisnon-limiting example, the target objects 1404 and non-target objects1406 differ in color. As shown in FIG. 14D, the instructions panel 1402can include a visual representation of the target object in addition tothe written instructions to the individual.

FIGS. 15A-15V show other examples of the dynamics of multi-taskinginvolving user interaction with an implementation of a navigation task,and with an interference. In this example, the task is a visuo-motornavigation task, and the interference is target discrimination (as asecondary task). The individual is instructed to perform the navigationtask by controlling the motion of the avatar 1502 along a path thatcoincides with the milestone objects 1504. FIGS. 15A-15V show anon-limiting example implementation where the individual is expected toactuate an apparatus or computing device (or other sensing device) tocause the avatar 1502 to coincide with the milestone object 1504 as theresponse in the navigation task, with scoring based on the success ofthe individual at hitting or otherwise crossing paths with the milestoneobjects 1504. In another example, the individual is expected to actuatean apparatus or computing device (or other sensing device) to cause theavatar 1502 to miss (i.e., not cross paths) with the milestone object1504, with scoring based on the success of the individual at avoidingthe milestone objects 1504. FIGS. 15A-15V also show the dynamics of atarget object 1506, where the time-varying characteristic is thetrajectory of motion of the target object 1506. FIGS. 15A-15V also showthe dynamics of a non-target object 1508, where the time-varyingcharacteristic is the trajectory of motion of the non-target object1508.

FIGS. 15K-15V show non-limiting examples of the types of rewards thatmay be rendered to an individual to signal a degree of success ininteracting with the tasks and/or interference. In FIGS. 15K-15R, afeature 1510 including the word “GOOD” is rendered near the avatar 1502to signal to the individual that analysis of the data indicative of theindividual's responses to the navigation task and target discriminationinterference indicate satisfactory performance. FIG. 15V shows anexample of a change in the type of rewards presented to the individualas another indication of satisfactory performance, including a change inthe rendering of feature 1500 to display the word “GREAT”, at least onemodification to the avatar 1502 to symbolize excitement, such as but notlimited to the rings 1504 or other active element and/or showing jetbooster elements 1506 that become star-shaped. Many other types ofreward elements can be used, and the rate and type of reward elementsdisplayed can be changed and modulated as a time-varying element.

As described hereinabove, the example systems, methods, and apparatusherein are configured to apply a computational model of humandecision-making to the received response data received, based on thetime-varying characteristics of the task and/or interference. Thetime-varying characteristics of the task and/or interference result innonlinear accumulation of belief for an individual's decision making.Accordingly, based on the response data from the individual'sinteraction with the task and/or interference, the processing unit canbe configured to compute at least one response profile representative ofthe performance of the individual and determines a decision boundarymetric (such as but not limited to the response criterion) from theresponse profile. As also described hereinabove, the response classifiercan be executed based on the computed values of decision boundary metric(such as but not limited to the response criterion), to generate aclassifier output indicative of the cognitive response capabilities ofthe individual.

In various examples, the degree of non-linearity of the accumulation ofbelief for an individual's decision making (i.e., as to whether toexecute a response) can be modulated based on adjusting the time-varyingcharacteristics of the task and/or interference. As a non-limitingexample, where the time-varying characteristic is a trajectory, speed,orientation, or size of the object (target or non-target), the amount ofinformation available to an individual to develop a belief (in order tomake decision as to whether to execute a response) can be made smallerinitially, e.g., where the object caused to be more difficult todiscriminate by being rendered as farther away or smaller, and can bemade to increase at differing rates (nonlinearly) depending on howquickly more information is made available to the individual to developbelief (e.g., as the object is rendered to appear to get larger, changeorientation, move slower, or move closer in the environment). Othernon-limiting example time-varying characteristics of the task and/orinterference that can be adjusted to modulate the degree ofnon-linearity of the accumulation of belief include one or more of arate of change of a facial expression, at least one color of an object,the type of the object, a rate of morphing of a first type of object tochange to a second type of object, and a proportionate amount of a firsttype of object and a second type of object that form a blendshape (e.g.,where the second type of object is the target and the first type ofobject is a non-target).

As also described hereinabove, the programmed processing unit can beconfigured to execute processor-executable instructions to adapt thetask and/or the interference to derive a modification in the computedresponse profile. Given that the response profile is computed based onthe individual's response data (e.g., data based on a first response tothe task and/or data based on a second response to the interference), achange in the computed response profile can be used as an indication ofa change in the responses and performance measures of the individual.This in turn can provide an indication of a modification of thecognitive response capabilities of the individual.

As described hereinabove, adapting the tasks and/or interference basedon the output from the response classifiers can provide for greaterflexibility than adaptation based on the percent correct or D-Prime (d′)signal detection metric of sensitivity to a target. That is, theinteraction is adapted by modifying parameters of the tasks and/orinterference to be rendered to the user interface in a subsequent trialor session of the individual's interaction based on the computeddecision boundary metric (such as but not limited to the responsecriterion) and/or the output from a trained response classifier usingresponse data from a previous trial or session. For example, if thedecision boundary metric (such as but not limited to the responsecriterion) indicates a tendency of the individual to provide a firsttype of response of the two or more differing types of responses(Response A vs. Response B) to the task or the interference, thedifficulty levels of the tasks and/or interference rendered at the userinterface for user interaction for a subsequent level can be modified.The methodology for adapting the difficulty levels of the task and/orinterference of a subsequent session based on the decision boundarymetric (such as but not limited to the response criterion) computed forthe individual's performance from a previous session can be optimized tomodify an individual's first decision boundary metric (such as but notlimited to the response criterion) (and first performance) indicative offirst type of response strategy towards a second decision boundarymetric (such as but not limited to the response criterion) (and secondperformance) indicative of a second type of response strategy.

As a non-limiting example, the difficulty level of a subsequent sessionof an implementation of an example system, method, and apparatus hereincan be adapted to modify an individual's first decision boundary metric(such as but not limited to the response criterion) (and firstperformance) indicative of a more impulsive response strategy to asecond decision boundary metric (such as but not limited to the responsecriterion) (and second performance) indicative of a more conservativeresponse strategy, thereby seeking to promote less impulsive behavior inthe individual.

In a non-limiting example, the adaptation of the difficulty of a taskand/or interference may be adapted with each different stimulus that ispresented.

In another non-limiting example, the example system, method, andapparatus herein can be configured to adapt a difficulty level of a taskand/or interference one or more times in fixed time intervals or inother set schedule, such as but not limited to each second, in 10 secondintervals, every 30 seconds, or on frequencies of once per second, 2times per second, or more (such as but not limited to 30 times persecond).

In an example, the difficulty level of a task or interference can beadapted by changing the time-varying characteristics, such as but notlimited to a speed of an object, a rate of change of a facialexpression, a direction of trajectory of an object, a change oforientation of an object, at least one color of an object, a type of anobject, or a size of an object, or changing a sequence or balance ofpresentation of a target stimulus versus a non-target stimulus.

In a non-limiting example of a visuo-motor task (a type of navigationtask), one or more of navigation speed, shape of the course (changingfrequency of turns, changing turning radius), and number or size ofobstacles can be changed to modify the difficulty of a navigation gamelevel, with the difficulty level increasing with increasing speed and/orincreasing numbers and/or sizes of obstacles (milestone objects).

In a non-limiting example, the difficulty level of a task and/orinterference of a subsequent level can also be changed in real-time asfeedback, e.g., the difficulty of a subsequent level can be increased ordecreased in relation to the data indicative of the performance of thetask.

FIG. 16A-16C show flowcharts of example methods, according to theprinciples herein. In any example, the method is executed based onexecution of processor-executable instructions using a programmedprocessing unit.

FIG. 16A shows a method for generating a quantifier of cognitive skillsin an individual using a machine learning response classifier, using aprogrammed processing unit. In block 1602, a task with an interferenceis rendered at a user interface, where the task and/or the interferenceis time-varying and has a response deadline, such that the userinterface imposes a limited time period for receiving at least one typeof response from an individual. In block 1604, data indicative of afirst response of an individual to the task and a second response of theindividual to the interference is received. In block 1606, the dataindicative of the first response and the second response are analyzed tocompute at least one response profile representative of a performance ofthe individual. In block 1608, a decision boundary metric is determinedfrom the response profile, where the decision boundary metric includes aquantitative measure of a tendency of the individual to provide at leastone type of response of the two or more differing types of responses tothe task or the interference. In block 1610, a response classifier isexecuted based on the computed values of decision boundary metric, togenerate a classifier output indicative of the cognitive responsecapabilities of the individual.

FIG. 16B shows a method for enhancing cognitive skills in an individual,using a programmed processing unit. In block 1612, a task with aninterference is rendered at a user interface, where the task and/or theinterference is time-varying and has a response deadline, such that theuser interface imposes a limited time period for receiving at least onetype of response from an individual. In block 1614, data indicative of afirst response of an individual to the task and a second response of theindividual to the interference is received. In block 1616, the dataindicative of the first response and the second response are analyzed tocompute at least one response profile representative of a performance ofthe individual. In block 1618, a decision boundary metric is determinedfrom the response profile, where the decision boundary metric includes aquantitative measure of a tendency of the individual to provide at leastone type of response of the two or more differing types of responses tothe task or the interference. In block 1620, based on the computed firstdecision boundary metric, the task and/or the interference is adapted toderive a modification in the computed at least one decision boundarymetric (such as but not limited to the response criterion) such that thefirst response and/or the second response is modified, therebyindicating a modification of the cognitive response capabilities of theindividual.

FIG. 16C shows a method for enhancing cognitive skills in an individual,using a programmed processing unit. In block 1622, data indicative ofone or more of an amount, concentration, or dose titration of apharmaceutical agent, drug, or biologic being or to be administered toan individual is received. In block 1624, a task with an interference isrendered at a user interface, where the task and/or the interference istime-varying and has a response deadline, such that the user interfaceimposes a limited time period for receiving at least one type ofresponse from an individual. In block 1626, data indicative of a firstresponse of an individual to the task and a second response of theindividual to the interference, from a first session, is received. Inblock 1628, the data indicative of the first response and the secondresponse is analyzed to compute a first response profile representativeof a first performance of the individual. In block 1630, a firstdecision boundary metric is determined based on the first responseprofile, where the first decision boundary metric includes aquantitative measure of a tendency of the individual to provide at leastone type of response of the two or more differing types of responses tothe interference. In block 1632, based on the computed first decisionboundary metric and the amount or concentration of a pharmaceuticalagent, drug, or biologic, the task and/or the interference is adapted togenerate a second session. In block 1634, the collected data indicativeof the first response and the second response from the second session isanalyzed to compute a second response profile and a second decisionboundary metric representative of a second performance of theindividual. In block 1636, based on the first decision boundary metricand second decision boundary metric, an output is generated to the userinterface indicative of one or more of: (i) a likelihood of theindividual experiencing an adverse event in response to administrationof the pharmaceutical agent, drug, or biologic, (ii) a recommendedchange in one or more of the amount, concentration, or dose titration ofthe pharmaceutical agent, drug, or biologic, and (iii) a change in theindividual's cognitive response capabilities, a recommended treatmentregimen, or recommending or determining a degree of effectiveness of atleast one of a behavioral therapy, counseling, or physical exercise

FIG. 17 is a block diagram of an example computing device 1710 that canbe used as a computing component according to the principles herein. Inany example herein, computing device 1710 can be configured as a consolethat receives user input to implement the computing component, includingto apply the signal detection metrics in computer-implemented adaptiveresponse-deadline procedures. For clarity, FIG. 17 also refers back toand provides greater detail regarding various elements of the examplesystem of FIG. 1 and the example computing device of FIG. 2 . Thecomputing device 1710 can include one or more non-transitorycomputer-readable media for storing one or more computer-executableinstructions or software for implementing examples. The non-transitorycomputer-readable media can include, but are not limited to, one or moretypes of hardware memory, non-transitory tangible media (for example,one or more magnetic storage disks, one or more optical disks, one ormore flash drives), and the like. For example, memory 302 included inthe computing device 1710 can store computer-readable andcomputer-executable instructions or software for performing theoperations disclosed herein. For example, the memory 302 can store asoftware application 1740 which is configured to perform various of thedisclosed operations (e.g., analyze cognitive platform measurement dataand response data, apply a signal detection metrics in adaptiveresponse-deadline procedures, or performing a computation). Thecomputing device 1710 also includes configurable and/or programmableprocessor 304 and an associated core 1714, and optionally, one or moreadditional configurable and/or programmable processing devices, e.g.,processor(s) 1712′ and associated core(s) 1714′ (for example, in thecase of computational devices having multiple processors/cores), forexecuting computer-readable and computer-executable instructions orsoftware stored in the memory 302 and other programs for controllingsystem hardware. Processor 304 and processor(s) 1712′ can each be asingle core processor or multiple core (1714 and 1714′) processor.

Virtualization can be employed in the computing device 1710 so thatinfrastructure and resources in the console can be shared dynamically. Avirtual machine 1724 can be provided to handle a process running onmultiple processors so that the process appears to be using only onecomputing resource rather than multiple computing resources. Multiplevirtual machines can also be used with one processor.

Memory 302 can include a computational device memory or random accessmemory, such as DRAM, SRAM, EDO RAM, and the like. Memory 302 caninclude other types of memory as well, or combinations thereof.

A user can interact with the computing device 1710 through a visualdisplay unit 1728, such as a computer monitor, which can display one ormore user interfaces (UI) 1730 that can be provided in accordance withexample systems and methods. The computing device 1710 can include otherI/O devices for receiving input from a user, for example, a keyboard orany suitable multi-point touch interface 1718, a pointing device 1720(e.g., a mouse). The keyboard 1718 and the pointing device 1720 can becoupled to the visual display unit 1728. The computing device 1710 caninclude other suitable conventional I/O peripherals.

The computing device 1710 can also include one or more storage devices1734, such as a hard-drive, CD-ROM, or other computer readable media,for storing data and computer-readable instructions and/or software thatperform operations disclosed herein. Example storage device 1734 canalso store one or more databases for storing any suitable informationrequired to implement example systems and methods. The databases can beupdated manually or automatically at any suitable time to add, delete,and/or update one or more items in the databases.

The computing device 1710 can include a network interface 1722configured to interface via one or more network devices 1732 with one ormore networks, for example, Local Area Network (LAN), Wide Area Network(WAN) or the Internet through a variety of connections including, butnot limited to, standard telephone lines, LAN or WAN links (for example,802.11, T1, T3, 56 kb, X.25), broadband connections (for example, ISDN,Frame Relay, ATM), wireless connections, controller area network (CAN),or some combination of any or all of the above. The network interface1722 can include a built-in network adapter, network interface card,PCMCIA network card, card bus network adapter, wireless network adapter,USB network adapter, modem or any other device suitable for interfacingthe computing device 1710 to any type of network capable ofcommunication and performing the operations described herein. Moreover,the computing device 1710 can be any computational device, such as aworkstation, desktop computer, server, laptop, handheld computer, tabletcomputer, or other form of computing or telecommunications device thatis capable of communication and that has sufficient processor power andmemory capacity to perform the operations described herein.

The computing device 1710 can run any operating system 1726, such as anyof the versions of the Microsoft® Windows® operating systems, thedifferent releases of the Unix and Linux operating systems, any versionof the MacOS® for Macintosh computers, any embedded operating system,any real-time operating system, any open source operating system, anyproprietary operating system, or any other operating system capable ofrunning on the console and performing the operations described herein.In some examples, the operating system 1726 can be run in native mode oremulated mode. In an example, the operating system 1726 can be run onone or more cloud machine instances.

Examples of the systems, methods and operations described herein can beimplemented in digital electronic circuitry, or in computer software,firmware, or hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more thereof. Examples of the systems, methods and operationsdescribed herein can be implemented as one or more computer programs,i.e., one or more modules of computer program instructions, encoded oncomputer storage medium for execution by, or to control the operationof, data processing apparatus. The program instructions can be encodedon an artificially generated propagated signal, e.g., amachine-generated electrical, optical, or electromagnetic signal, thatis generated to encode information for transmission to suitable receiverapparatus for execution by a data processing apparatus. A computerstorage medium can be, or be included in, a computer-readable storagedevice, a computer-readable storage substrate, a random or serial accessmemory array or device, or a combination of one or more of them.Moreover, while a computer storage medium is not a propagated signal, acomputer storage medium can be a source or destination of computerprogram instructions encoded in an artificially generated propagatedsignal. The computer storage medium can also be, or be included in, oneor more separate physical components or media (e.g., multiple CDs,disks, or other storage devices).

The operations described in this specification can be implemented asoperations performed by a data processing apparatus on data stored onone or more computer-readable storage devices or received from othersources.

The term “data processing apparatus” or “computing device” encompassesall kinds of apparatus, devices, and machines for processing data,including by way of example a programmable processor, a computer, asystem on a chip, or multiple ones, or combinations, of the foregoing.The apparatus can include special purpose logic circuitry, e.g., an FPGA(field programmable gate array) or an ASIC (application specificintegrated circuit). The apparatus can also include, in addition tohardware, code that creates an execution environment for the computerprogram in question, e.g., code that constitutes processor firmware, aprotocol stack, a database management system, an operating system, across-platform runtime environment, a virtual machine, or a combinationof one or more of them.

A computer program (also known as a program, software, softwareapplication, script, application or code) can be written in any form ofprogramming language, including compiled or interpreted languages,declarative or procedural languages, and it can be deployed in any form,including as a stand alone program or as a module, component,subroutine, object, or other unit suitable for use in a computingenvironment. A computer program may, but need not, correspond to a filein a file system. A program can be stored in a portion of a file thatholds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing on one ormore computer programs to perform actions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatuses can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for performing actions in accordance with instructions andone or more memory devices for storing instructions and data. Generally,a computer will also include, or be operatively coupled to receive datafrom or transfer data to, or both, one or more mass storage devices forstoring data, e.g., magnetic, magneto-optical disks, or optical disks.However, a computer need not have such devices. Moreover, a computer canbe embedded in another device, e.g., a mobile telephone, a personaldigital assistant (PDA), a mobile audio or video player, a game console,a Global Positioning System (GPS) receiver, or a portable storage device(e.g., a universal serial bus (USB) flash drive), for example. Devicessuitable for storing computer program instructions and data include allforms of non volatile memory, media and memory devices, including by wayof example semiconductor memory devices, e.g., EPROM, EEPROM, and flashmemory devices; magnetic disks, e.g., internal hard disks or removabledisks; magneto-optical disks; and CD ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated in,special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse, a stylus, touch screen ora trackball, by which the user can provide input to the computer. Otherkinds of devices can be used to provide for interaction with a user aswell. For example, feedback (i.e., output) provided to the user can beany form of sensory feedback, e.g., visual feedback, auditory feedback,or tactile feedback; and input from the user can be received in anyform, including acoustic, speech, or tactile input. In addition, acomputer can interact with a user by sending documents to and receivingdocuments from a device that is used by the user; for example, bysending web pages to a web browser on a user's client device in responseto requests received from the web browser.

In some examples, a system, method or operation herein can beimplemented in a computing system that includes a back end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification, or any combination of one ormore such back end, middleware, or front end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), an inter-network (e.g., the Internet), andpeer-to-peer networks (e.g., ad hoc peer-to-peer networks).

Example computing system 400 can include clients and servers. A clientand server are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someembodiments, a server transmits data to a client device (e.g., forpurposes of displaying data to and receiving user input from a userinteracting with the client device). Data generated at the client device(e.g., a result of the user interaction) can be received from the clientdevice at the server.

CONCLUSION

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinventions or of what may be claimed, but rather as descriptions offeatures specific to particular embodiments of the systems and methodsdescribed herein. Certain features that are described in thisspecification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable subcombination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In some cases, the actions recited in the claims can beperformed in a different order and still achieve desirable results. Inaddition, the processes depicted in the accompanying figures do notnecessarily require the particular order shown, or sequential order, toachieve desirable results.

In certain circumstances, multitasking and parallel processing may beadvantageous. Moreover, the separation of various system components inthe embodiments described above should not be understood as requiringsuch separation in all embodiments, and it should be understood that thedescribed program components and systems can generally be integratedtogether in a single software product or packaged into multiple softwareproducts

The above-described embodiments can be implemented in any of numerousways. For example, some embodiments may be implemented using hardware,software or a combination thereof. When any aspect of an embodiment isimplemented at least in part in software, the software code can beexecuted on any suitable processor or collection of processors, whetherprovided in a single computer or distributed among multiple computers.

The above-described embodiments can be implemented in any of numerousways. For example, some embodiments may be implemented using hardware,software or a combination thereof. When any aspect of an embodiment isimplemented at least in part in software, the software code can beexecuted on any suitable processor or collection of processors, whetherprovided in a single computer or distributed among multiple computers.

In this respect, various aspects of the invention may be embodied atleast in part as a computer readable storage medium (or multiplecomputer readable storage media) (e.g., a computer memory, compactdisks, optical disks, magnetic tapes, flash memories, circuitconfigurations in Field Programmable Gate Arrays or other semiconductordevices, or other tangible computer storage medium or non-transitorymedium) encoded with one or more programs that, when executed on one ormore computers or other processors, perform methods that implement thevarious embodiments of the technology discussed above. The computerreadable medium or media can be transportable, such that the program orprograms stored thereon can be loaded onto one or more differentcomputers or other processors to implement various aspects of thepresent technology as discussed above.

The terms “program” or “software” are used herein in a generic sense torefer to any type of computer code or set of computer-executableinstructions that can be employed to program a computer or otherprocessor to implement various aspects of the present technology asdiscussed above. Additionally, it should be appreciated that accordingto one aspect of this embodiment, one or more computer programs thatwhen executed perform methods of the present technology need not resideon a single computer or processor, but may be distributed in a modularfashion amongst a number of different computers or processors toimplement various aspects of the present technology.

Computer-executable instructions may be in many forms, such as programmodules, executed by one or more computers or other devices. Generally,program modules include routines, programs, objects, components, datastructures, etc. that perform particular tasks or implement particularabstract data types. Typically the functionality of the program modulesmay be combined or distributed as desired in various embodiments.

Also, the technology described herein may be embodied as a method, ofwhich at least one example has been provided. The acts performed as partof the method may be ordered in any suitable way. Accordingly,embodiments may be constructed in which acts are performed in an orderdifferent than illustrated, which may include performing some actssimultaneously, even though shown as sequential acts in illustrativeembodiments.

All definitions, as defined and used herein, should be understood tocontrol over dictionary definitions, definitions in documentsincorporated by reference, and/or ordinary meanings of the definedterms.

The indefinite articles “a” and “an,” as used herein in thespecification and in the claims, unless clearly indicated to thecontrary, should be understood to mean “at least one.”

The phrase “and/or,” as used herein in the specification and in theclaims, should be understood to mean “either or both” of the elements soconjoined, i.e., elements that are conjunctively present in some casesand disjunctively present in other cases. Multiple elements listed with“and/or” should be construed in the same fashion, i.e., “one or more” ofthe elements so conjoined. Other elements may optionally be presentother than the elements specifically identified by the “and/or” clause,whether related or unrelated to those elements specifically identified.Thus, as a non-limiting example, a reference to “A and/or B”, when usedin conjunction with open-ended language such as “comprising” can refer,in one embodiment, to A only (optionally including elements other thanB); in another embodiment, to B only (optionally including elementsother than A); in yet another embodiment, to both A and B (optionallyincluding other elements); etc.

As used herein in the specification and in the claims, “or” should beunderstood to have the same meaning as “and/or” as defined above. Forexample, when separating items in a list, “or” or “and/or” shall beinterpreted as being inclusive, i.e., the inclusion of at least one, butalso including more than one, of a number or list of elements, and,optionally, additional unlisted items. Only terms clearly indicated tothe contrary, such as “only one of” or “exactly one of,” or, when usedin the claims, “consisting of,” will refer to the inclusion of exactlyone element of a number or list of elements. In general, the term “or”as used herein shall only be interpreted as indicating exclusivealternatives (i.e. “one or the other but not both”) when preceded byterms of exclusivity, such as “either,” “one of,” “only one of,” or“exactly one of.” “Consisting essentially of,” when used in the claims,shall have its ordinary meaning as used in the field of patent law.

As used herein in the specification and in the claims, the phrase “atleast one,” in reference to a list of one or more elements, should beunderstood to mean at least one element selected from any one or more ofthe elements in the list of elements, but not necessarily including atleast one of each and every element specifically listed within the listof elements and not excluding any combinations of elements in the listof elements. This definition also allows that elements may optionally bepresent other than the elements specifically identified within the listof elements to which the phrase “at least one” refers, whether relatedor unrelated to those elements specifically identified. Thus, as anon-limiting example, “at least one of A and B” (or, equivalently, “atleast one of A or B,” or, equivalently “at least one of A and/or B”) canrefer, in one embodiment, to at least one, optionally including morethan one, A, with no B present (and optionally including elements otherthan B); in another embodiment, to at least one, optionally includingmore than one, B, with no A present (and optionally including elementsother than A); in yet another embodiment, to at least one, optionallyincluding more than one, A, and at least one, optionally including morethan one, B (and optionally including other elements); etc.

In the claims, as well as in the specification above, all transitionalphrases such as “comprising,” “including,” “carrying,” “having,”“containing,” “involving,” “holding,” “composed of,” and the like are tobe understood to be open-ended, i.e., to mean including but not limitedto. Only the transitional phrases “consisting of” and “consistingessentially of” shall be closed or semi-closed transitional phrases,respectively, as set forth in the United States Patent Office Manual ofPatent Examining Procedures, Section 2111.03.

What is claimed is:
 1. An apparatus for generating a quantifier ofcognitive skills in an individual using a response classifier, saidapparatus comprising: a user interface; a memory to storeprocessor-executable instructions; and a processing unit communicativelycoupled to the user interface and the memory, wherein upon execution ofthe processor-executable instructions by the processing unit, theprocessing unit is configured to: render a task with an interference atthe user interface, one or more of the task and the interference beingtime-varying and having a response deadline, such that the userinterface imposes a limited time period for receiving at least one typeof response from an individual, wherein the user interface is configuredto measure data indicative of two or more differing types of responsesto the task or to the interference; receive data indicative of a firstresponse of an individual to the task and a second response of theindividual to the interference at the user interface; analyze the dataindicative of the first response and the second response to compute atleast one response profile representative of a performance of theindividual; determine a decision boundary metric from the at least oneresponse profile, the decision boundary metric comprising a quantitativemeasure of a tendency of the individual to provide at least one of animpulsive response to the task with the interference and a conservativeresponse to the task with the interference; execute the responseclassifier based at least in part on the computed values of the decisionboundary metric to generate a classifier output indicative of thecognitive response capabilities of the individual; and apply at leastone adaptive procedure to modify the task and/or the interference at theuser interface, such that analysis of the data indicative of the firstresponse and/or the second response indicates a modification of the atleast one response profile, wherein the at least one adaptive procedureis configured to modify a time-varying characteristic of the task and/orthe interference at the user interface, wherein the at least oneadaptive procedure modifies the response deadline to modify aperformance characteristic of the individual to an impulsive responseprofile or a conservative response profile, such that measurements atdifferent response deadlines provide different data as to a firstdecision boundary or a second decision boundary of the decision boundarymetric, wherein modifying the time-varying characteristic of the taskand/or the interference at the user interface is configured to modifythe first decision boundary or the second decision boundary of thedecision boundary metric, wherein the impulsive response profilerepresents an impulsive response strategy of the individual to the taskor the interference, and the conservative response profile represents aconservative response strategy of the individual to the task or theinterference.
 2. The apparatus of claim 1, further comprising at leastone actuating component, wherein the processing unit further controlsthe actuating component to effect one or more of an auditory stimulus, atactile stimulus, and a vibrational stimulus, and wherein the taskand/or the interference comprises one or more of the auditory stimulus,the tactile stimulus, and the vibrational stimulus.
 3. The apparatus ofclaim 1, wherein the processing unit is further configured to compute asthe classifier output parameters indicative of one or more of a biassensitivity derived from the data indicative of the first response andthe second response, a non-decision time sensitivity to parallel tasks,a belief accumulation sensitivity to parallel task demands, a rewardrate sensitivity, or a response window estimation efficiency.
 4. Theapparatus of claim 1, wherein the processing unit is configured tocontrol the user interface to render the task as a continuousvisuo-motor tracking task.
 5. The apparatus of claim 1, wherein theprocessing unit is configured to control the user interface to renderthe interference as a target discrimination interference.
 6. Theapparatus of claim 1, wherein the processing unit is configured torender the task with the interference by configuring the user interfaceto: render the task in the presence of the interference such that theinterference diverts the individual's attention from the task, in whichthe interference is selected from a group consisting of a distractionand an interrupter.
 7. The apparatus of claim 1, wherein the responseclassifier is trained using a plurality of training datasets, eachtraining dataset corresponding to a previously classified individual ofa plurality of individuals, and each training dataset comprising dataindicative of a first response of the classified individual to the task,data indicative of a second response of the classified individual to theinterference, and one or more of (i) data indicative of a performance ofthe classified individual at one or more of a cognitive test or abehavioral test, and (ii) data indicative of a diagnosis of a status orprogression of a cognitive condition, a disease or an executive functiondisorder of the classified individual.
 8. The apparatus of claim 1,wherein the classifier output comprises an indication of a degree ofimpulsiveness or conservativeness of the individual's cognitive responsecapabilities.
 9. The apparatus of claim 1, wherein the processing unitis configured to transmit the classifier output to the individual and/ordisplay the classifier output on the user interface.
 10. The apparatusof claim 1, wherein the classifier output comprises a measure ofattention deficit or impulsivity of the individual.
 11. The apparatus ofclaim 1, wherein the processing unit is further configured to use theclassifier output for cognitive monitoring of one or more of a cognitivecondition, a disease, or an executive function disorder.
 12. Theapparatus of claim 1, wherein the apparatus comprises one or more sensorcomponents, and wherein the processing unit is configured to control theone or more sensor components to receive the data indicative of thefirst response and the second response.
 13. The apparatus of claim 1,wherein the processing unit is further configured to use the classifieroutput for one or more of (i) changing one or more of a recommendedamount, concentration, or dose titration of a pharmaceutical agent,drug, or biologic, (ii) identifying a likelihood of the individualexperiencing an adverse event in response to administration of thepharmaceutical agent, drug, or biologic, (iii) identifying a change inthe individual's cognitive response capabilities, (iv) recommending atreatment regimen, (v) recommending at least one of a behavioraltherapy, counseling, or physical exercise, or (vi) determining a degreeof effectiveness of at least one of a behavioral therapy, counseling, orphysical exercise.
 14. A computer-implemented method for generating aquantifier of cognitive skills in an individual using a responseclassifier, said method comprising: rendering, using at least oneprocessing unit, a task with an interference at a user interface;measuring data indicative of two or more differing types of responses tothe task or to the interference at the user interface; receiving, viathe user interface, data indicative of a first response of an individualto the task and a second response of the individual to the interference;analyzing, using the at least one processing unit, the data indicativeof the first response and the second response to compute at least oneresponse profile representative of a performance of the individual;determining, using the at least one processing unit, a decision boundarymetric from the response profile, the decision boundary metriccomprising a quantitative measure of a tendency of the individual toprovide at least one of an impulsive response to the task with theinterference and a conservative response to the task with theinterference; executing the response classifier based at least in parton the decision boundary metric, to generate a classifier outputindicative of the individual's cognitive response capabilities; andapplying, with the at least one processing unit, at least one adaptiveprocedure to modify the task and/or the interference at the userinterface, such that analysis of the data indicative of the firstresponse and/or the second response indicates a modification of the atleast one response profile, wherein the at least one adaptive procedureis configured to modify a time-varying characteristic of the task and/orinterference at the user interface, wherein the at least one adaptiveprocedure modifies the response deadline to modify a performancecharacteristic of the individual to an impulsive response profile or aconservative response profile, such that measurements at differentresponse deadlines provide different data as to a first decisionboundary or a second decision boundary of the decision boundary metric,wherein modifying the time-varying characteristic of the task and/or theinterference at the user interface is configured to modify the firstdecision boundary or the second decision boundary of the decisionboundary metric, wherein the impulsive response profile represents animpulsive response strategy of the individual to the task or theinterference, and the conservative response profile represents aconservative response strategy of the individual to the task or theinterference.
 15. The method of claim 14, wherein the at least oneprocessing unit is further configured to control at least one actuatingcomponent to effect one or more of an auditory stimulus, a tactilestimulus, and a vibrational stimulus, and wherein the task and/or theinterference comprises one or more of the auditory stimulus, the tactilestimulus, and the vibrational stimulus.
 16. The method of claim 14,wherein the at least one processing unit is further configured tocompute as the classifier output parameters indicative of one or more ofa bias sensitivity derived from the data indicative of the firstresponse and the second response, a non-decision time sensitivity toparallel tasks, a belief accumulation sensitivity to parallel taskdemands, a reward rate sensitivity, or a response window estimationefficiency.
 17. The method of claim 14, wherein the at least oneprocessing unit is configured to control the user interface to renderthe task as a continuous visuo-motor tracking task.
 18. The method ofclaim 14, wherein the at least one processing unit is configured tocontrol the user interface to render the interference as a targetdiscrimination interference.
 19. The method of claim 14, whereinrendering the task with the interference comprises: rendering the taskin the presence of the interference such that the interference divertsthe individual's attention from the task, the interference selected fromthe group consisting of a distraction and an interrupter.
 20. The methodof claim 14, wherein the response classifier is trained using aplurality of training datasets, each training dataset corresponding to apreviously classified individual of a plurality of individuals, and eachtraining dataset comprising data indicative of the first response of theclassified individual to the task, data indicative of the secondresponse of the classified individual to the interference, and one ormore of (i) data indicative of a performance of the classifiedindividual at one or more of a cognitive test or a behavioral test, and(ii) data indicative of a diagnosis of a status or progression of acognitive condition, a disease or an executive function disorder of theclassified individual.
 21. The method of claim 14, wherein theclassifier output comprises an indication of a degree of impulsivenessor conservativeness of the individual's cognitive response capabilities.22. The method of claim 14, wherein the classifier output is transmittedto the individual and/or displayed to the user interface.
 23. The methodof claim 14, wherein the classifier output comprises a measure ofattention deficit or impulsivity of the individual.
 24. The method ofclaim 14, further comprising using the classifier output for cognitivemonitoring of one or more of a cognitive condition, a disease, or anexecutive function disorder.
 25. The method of claim 14, whereinreceiving the data indicative of the first response and the secondresponse comprises using one or more sensor components to receive thedata indicative of the first response and the second response.
 26. Themethod of claim 14, further comprising using the classifier output forone or more of changing one or more of an amount, concentration, or dosetitration of a pharmaceutical agent, drug, or biologic, identifying alikelihood of the individual experiencing an adverse event in responseto administration of the pharmaceutical agent, drug, or biologic,identifying a change in the individual's cognitive responsecapabilities, recommending a treatment regimen, or recommending ordetermining a degree of effectiveness of at least one of a behavioraltherapy, counseling, or physical exercise.
 27. An apparatus forenhancing cognitive skills in an individual, said apparatus comprising:a user interface; a memory to store processor-executable instructions;and a processing unit communicatively coupled to the user interface andthe memory, wherein upon execution of the processor-executableinstructions by the processing unit, the processing unit is configuredto: render a primary task with an interference at the user interface,one or more of the primary task and the interference being time-varyingand having a response deadline, such that the user interface imposes alimited time period for receiving at least one type of response from anindividual; and the user interface being configured to measure dataindicative of two or more differing types of responses to the primarytask or to the interference; receive data indicative of a first responseof an individual to the primary task and a second response of theindividual to the interference at the user interface; analyze the dataindicative of the first response and the second response to compute atleast one response profile representative of a performance of theindividual; determine a decision boundary metric based at least in parton the at least one response profile, the decision boundary metriccomprising a quantitative measure of a tendency of the individual toprovide at least one of an impulsive response to the task with theinterference and a conservative response to the task with theinterference; based at least in part on the computed decision boundarymetric, adapt the primary task and/or the interference to derive amodification in the computed decision boundary metric such that afurther response to the primary task and/or a further response to theinterference is modified at the user interface as compared to an earlierresponse to the primary task and/or an earlier response to theinterference, thereby indicating a modification of the cognitiveresponse capabilities of the individual; and apply at least one adaptiveprocedure to modify the primary task and/or the interference at the userinterface, such that analysis of the data indicative of the firstresponse and/or the second response indicates a modification of the atleast one response profile, wherein the at least one adaptive procedureis configured to modify a time-varying characteristic of the task and/orinterference at the user interface, wherein the at least one adaptiveprocedure modifies the response deadline to modify a performancecharacteristic of the individual to an impulsive response profile or aconservative response profile, such that measurements at differentresponse deadlines provide different data as to a first decisionboundary or a second decision boundary of the decision boundary metric,wherein modifying the time-varying characteristic of the task and/or theinterference at the user interface is configured to modify the firstdecision boundary or the second decision boundary of the decisionboundary metric, wherein the impulsive response profile represents animpulsive response strategy of the individual to the task or theinterference, and the conservative response profile represents aconservative response strategy of the individual to the task or theinterference.
 28. The apparatus of claim 27, wherein the indication ofthe modification of the cognitive response capabilities comprises achange in a measure of a degree of impulsiveness or conservativeness ofthe individual's cognitive response capabilities.
 29. The apparatus ofclaim 27, wherein the indication of the modification of the cognitiveresponse capabilities comprises a change in a measure of one or more ofsustained attention, selective attention, attention deficit,impulsivity, inhibition, perceptive abilities, reaction and other motorfunctions, visual acuity, long-term memory, working memory, short-termmemory, logic, and decision-making.
 30. The apparatus of claim 27,wherein adapting the primary task and/or the interference based at leastin part on the decision boundary metric comprises one or more ofmodifying a temporal length of a response window, modifying a type ofreward or rate of presentation of rewards to the individual, andmodifying a time-varying characteristic of the primary task and/or theinterference.
 31. The apparatus of claim 27, wherein the processing unitis configured to render the primary task with the interference byconfiguring the user interface to: render the primary task in thepresence of the interference such that the interference diverts theindividual's attention from the primary task and is selected from thegroup consisting of a distraction and an interrupter.
 32. Acomputer-implemented method for enhancing cognitive skills in anindividual, said method comprising: rendering a task with aninterference at a user interface; measuring data indicative of two ormore differing types of responses to the task or to the interference;receiving, via the user interface, data indicative of a first responseof the individual to the task and a second response of the individual tothe interference; analyzing, using at least one processing unit, thedata indicative of the first response and the second response to computeat least one response profile representative of a performance of theindividual; determining a decision boundary metric based at least inpart on the at least one response profile, the decision boundary metriccomprising a quantitative measure of a tendency of the individual toprovide at least one of an impulsive response to the task with theinterference and a conservative response to the task with theinterference; based at least in part on the determined decision boundarymetric, adapting the task and/or the interference to derive amodification in the determined decision boundary metric such that thefirst response and/or the second response is modified, therebyindicating a modification of the cognitive response capabilities of theindividual; and applying at least one adaptive procedure to modify thetask and/or the interference at the user interface, such that analysisof the data indicative of the first response and/or the second responseindicates a modification of the at least one response profile, whereinthe at least one adaptive procedure is configured to modify atime-varying characteristic of the task and/or the interference at theuser interface, wherein the at least one adaptive procedure modifies theresponse deadline to modify a performance characteristic of theindividual to an impulsive response profile or a conservative responseprofile, such that measurements at different response deadlines providedifferent data as to a first decision boundary or a second decisionboundary of the decision boundary metric, wherein modifying thetime-varying characteristic of the task and/or the interference at theuser interface is configured to modify the first decision boundary orthe second decision boundary of the decision boundary metric, whereinthe impulsive response profile represents an impulsive response strategyof the individual to the task or the interference, and the conservativeresponse profile represents a conservative response strategy of theindividual to the task or the interference.